Read in phenotypic data
# Read in data
pheno <- read.csv("NC13955_Combined_Data_Final.csv")
# Read in potential selfs
selfs <- read.csv("list_of_potential_selfs.csv")
# Remove selfs
pheno <- pheno %>%
filter(!GENOTYPE %in% selfs$GENOTYPE)
# Ensure the correct data types
pheno[, 1:7] <- lapply(pheno[, 1:7], as.factor)
pheno[, 8:ncol(pheno)] <- lapply(pheno[, 8:ncol(pheno)], as.numeric)
Calculate BLUEs within and across environments
# Set up lists for loop
env <- levels(pheno$ENV)
traits <- colnames(pheno)[8:12]
# Create dataframe for means output
BLUEs_loc <- data.frame(GENOTYPE = unique(pheno$GENOTYPE))
# Run models for each environment
for (i in env) {
for (j in traits) {
# Print message
print_message <- paste("### Analyzing", j, "in", i, "###")
print(paste(rep("#", nchar(print_message)), collapse = ""))
print(print_message)
print(paste(rep("#", nchar(print_message)), collapse = ""))
# Read subset of phenotype data
pheno_subset <- na.omit(pheno[pheno$ENV == i, c("GENOTYPE", "REP", j)])
colnames(pheno_subset)[3] <- "Y"
# Check if subset is empty
if (nrow(na.omit(pheno_subset)) == 0) {
# Throw message
print(paste("###", j, "not taken in", i, "moving on..."))
remove(pheno_subset)
next
}
if (j %in% c("FHB", "FDK", "DON")) {
# Run model and pull estimates
fit <- asreml(fixed = Y ~ GENOTYPE,
random = ~ REP,
residual = ~ units,
data = pheno_subset,
family = asr_poisson(),
maxit = 75)
# Check if model converges and if it doesn't, default to RCBD with normal distribution
if (fit$converge == FALSE | (i == "WAR20" & j == "FHB")) {
# Fit model
fit <- asreml(fixed = Y ~ GENOTYPE,
random = ~ REP,
residual = ~ units,
data = pheno_subset,
maxit = 75)
# Update model
fit <- update.asreml(fit)
}
# Get quick summary
print(summary(fit))
print(plot(fit))
# Pull BLUEs
pred <- predict(fit, classify = "GENOTYPE", maxit = 75)$pvals[, c("GENOTYPE", "predicted.value")]
# Rename
colnames(pred)[2] <- paste(j, "_", i, sep = "")
# Bind
BLUEs_loc <- left_join(BLUEs_loc, pred, by = "GENOTYPE")
} else {
# Run model and pull estimates
fit <- asreml(fixed = Y ~ GENOTYPE,
random = ~ REP,
residual = ~ units,
data = pheno_subset)
# Get BLUEs
pred <- predict(fit, classify = "GENOTYPE")$pvals[, c("GENOTYPE", "predicted.value")]
# Rename
colnames(pred)[2] <- paste(j, "_", i, sep = "")
# Bind
BLUEs_loc <- left_join(BLUEs_loc, pred, by = "GENOTYPE")
}
# Remove temporary objects
remove(pheno_subset, fit, pred)
}
}
## [1] "#############################"
## [1] "### Analyzing HD in KIN19 ###"
## [1] "#############################"
## [1] "### HD not taken in KIN19 moving on..."
## [1] "##############################"
## [1] "### Analyzing FHB in KIN19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:08
## LogLik Sigma2 DF wall
## 1 21.74295 1.0 131 11:49:08 ( 1 restrained)
## 2 22.95430 1.0 131 11:49:08 ( 1 restrained)
## 3 23.50873 1.0 131 11:49:08 ( 1 restrained)
## 4 23.57320 1.0 131 11:49:08 ( 1 restrained)
## 5 23.57756 1.0 131 11:49:08 ( 1 restrained)
## 6 23.57785 1.0 131 11:49:08
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 23.72
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 131 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 23.57785
##
## $nedf
## [1] 131
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 1.011929e-07 NA NA B NA
## units!R 1.000000e+00 NA NA F 0
##
## $bic
## [1] -42.28049
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -45.15569
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 23.72133
##
## $heterogeneity
## [1] 0.1810788
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:09
## LogLik Sigma2 DF wall
## 1 23.59471 1.0 131 11:49:09
## 2 23.57785 1.0 131 11:49:09
## 3 23.57785 1.0 131 11:49:09
## 4 23.57785 1.0 131 11:49:09
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 23.72
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 131 df.
## [1] "##############################"
## [1] "### Analyzing FDK in KIN19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:09
## LogLik Sigma2 DF wall
## 1 -199.9475 1.0 139 11:49:09 ( 1 restrained)
## 2 -199.6005 1.0 139 11:49:09 ( 1 restrained)
## 3 -199.6826 1.0 139 11:49:09
## 4 -199.3892 1.0 139 11:49:09
## 5 -199.3125 1.0 139 11:49:09
## 6 -199.3016 1.0 139 11:49:09
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 823.20
## : Variance heterogenity factor (Deviance/df): 5.92 assuming 139 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -199.3016
##
## $nedf
## [1] 139
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.001413535 0.002233495 0.6328804 P 2.5
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] 403.5377
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 400.6032
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 823.1962
##
## $heterogeneity
## [1] 5.922275
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:10
## LogLik Sigma2 DF wall
## 1 -198.4026 1.0 139 11:49:10
## 2 -199.3009 1.0 139 11:49:10
## 3 -199.3009 1.0 139 11:49:10
## 4 -199.3009 1.0 139 11:49:10
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 823.19
## : Variance heterogenity factor (Deviance/df): 5.92 assuming 139 df.
## [1] "##############################"
## [1] "### Analyzing DON in KIN19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:10
## LogLik Sigma2 DF wall
## 1 -102.9094 1.0 139 11:49:10 ( 1 restrained)
## 2 -126.8995 1.0 139 11:49:10
## 3 -130.6535 1.0 139 11:49:10
## 4 -130.6611 1.0 139 11:49:10
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 571.66
## : Variance heterogenity factor (Deviance/df): 4.11 assuming 139 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -130.6611
##
## $nedf
## [1] 139
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.009194348 0.01132679 0.811735 P 11.4
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] 266.2566
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 263.3221
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 571.6594
##
## $heterogeneity
## [1] 4.112658
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:11
## LogLik Sigma2 DF wall
## 1 -101.9381 1.0 139 11:49:11
## 2 -126.9168 1.0 139 11:49:11
## 3 -130.6750 1.0 139 11:49:11
## 4 -130.6760 1.0 139 11:49:11
## 5 -130.6764 1.0 139 11:49:11
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 571.65
## : Variance heterogenity factor (Deviance/df): 4.11 assuming 139 df.
## [1] "#############################"
## [1] "### Analyzing PH in KIN19 ###"
## [1] "#############################"
## [1] "### PH not taken in KIN19 moving on..."
## [1] "#############################"
## [1] "### Analyzing HD in KIN20 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:11
## LogLik Sigma2 DF wall
## 1 -292.7789 3.203751 211 11:49:11 ( 1 restrained)
## 2 -291.7829 3.206980 211 11:49:11 ( 1 restrained)
## 3 -291.5504 3.210761 211 11:49:11 ( 1 restrained)
## 4 -291.5335 3.211284 211 11:49:11 ( 1 restrained)
## 5 -291.5324 3.211319 211 11:49:11 ( 1 restrained)
## 6 -291.5323 3.211322 211 11:49:11
## ASReml Version 4.2 05/07/2024 11:49:11
## LogLik Sigma2 DF wall
## 1 -291.5323 3.211322 211 11:49:11
## 2 -291.5323 3.211322 211 11:49:11
## 3 -291.5323 3.211322 211 11:49:11
## [1] "##############################"
## [1] "### Analyzing FHB in KIN20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:11
## LogLik Sigma2 DF wall
## 1 47.29327 1.0 210 11:49:11 ( 1 restrained)
## 2 48.24621 1.0 210 11:49:11
## 3 48.24461 1.0 210 11:49:11
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 84.20
## : Variance heterogenity factor (Deviance/df): 0.40 assuming 210 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 48.24461
##
## $nedf
## [1] 210
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.006077932 0.01023055 0.5940961 P 0.1
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] -91.14211
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -94.48922
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 84.20462
##
## $heterogeneity
## [1] 0.4009744
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:12
## LogLik Sigma2 DF wall
## 1 48.25658 1.0 210 11:49:12
## 2 48.24448 1.0 210 11:49:12
## 3 48.24448 1.0 210 11:49:12
## 4 48.24448 1.0 210 11:49:12
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 84.20
## : Variance heterogenity factor (Deviance/df): 0.40 assuming 210 df.
## [1] "##############################"
## [1] "### Analyzing FDK in KIN20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:12
## LogLik Sigma2 DF wall
## 1 -94.78738 1.0 212 11:49:12 ( 1 restrained)
## 2 -93.86753 1.0 212 11:49:12
## 3 -93.89109 1.0 212 11:49:12
## 4 -93.81959 1.0 212 11:49:12
## 5 -93.81207 1.0 212 11:49:12
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 855.33
## : Variance heterogenity factor (Deviance/df): 4.03 assuming 212 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -93.81207
##
## $nedf
## [1] 212
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.003800331 0.005320064 0.7143393 P 2.9
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] 192.9807
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 189.6241
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 855.3259
##
## $heterogeneity
## [1] 4.034556
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:13
## LogLik Sigma2 DF wall
## 1 -93.64007 1.0 212 11:49:13
## 2 -93.81209 1.0 212 11:49:13
## 3 -93.81210 1.0 212 11:49:13
## 4 -93.81210 1.0 212 11:49:13
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 855.32
## : Variance heterogenity factor (Deviance/df): 4.03 assuming 212 df.
## [1] "##############################"
## [1] "### Analyzing DON in KIN20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:13
## LogLik Sigma2 DF wall
## 1 12.18138 1.0 167 11:49:14 ( 1 restrained)
## 2 10.39413 1.0 167 11:49:14
## 3 10.53455 1.0 167 11:49:14
## 4 10.61290 1.0 167 11:49:14
## 5 10.62106 1.0 167 11:49:14
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 248.45
## : Variance heterogenity factor (Deviance/df): 1.49 assuming 167 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 10.62106
##
## $nedf
## [1] 167
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.01998226 0.02337532 0.8548443 P 13.4
## units!R 1.00000000 NA NA F 0.0
##
## $bic
## [1] -16.12413
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -19.24212
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 248.4495
##
## $heterogeneity
## [1] 1.487722
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:14
## LogLik Sigma2 DF wall
## 1 12.66815 1.0 167 11:49:14
## 2 10.60842 1.0 167 11:49:14
## 3 10.60717 1.0 167 11:49:14
## 4 10.60621 1.0 167 11:49:15
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 248.44
## : Variance heterogenity factor (Deviance/df): 1.49 assuming 167 df.
## [1] "#############################"
## [1] "### Analyzing PH in KIN20 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:15
## LogLik Sigma2 DF wall
## 1 -451.0904 13.78991 213 11:49:15 ( 1 restrained)
## 2 -450.4711 13.85287 213 11:49:15
## 3 -450.4693 13.84757 213 11:49:15
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## ASReml Version 4.2 05/07/2024 11:49:15
## LogLik Sigma2 DF wall
## 1 -450.4693 13.84715 213 11:49:15
## 2 -450.4693 13.84715 213 11:49:15
## 3 -450.4693 13.84715 213 11:49:15
## [1] "#############################"
## [1] "### Analyzing HD in RAL19 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:15
## LogLik Sigma2 DF wall
## 1 -227.2095 2.609205 172 11:49:15
## 2 -226.9605 2.623647 172 11:49:15
## 3 -226.9485 2.620585 172 11:49:15
## 4 -226.9481 2.620018 172 11:49:15
## ASReml Version 4.2 05/07/2024 11:49:15
## LogLik Sigma2 DF wall
## 1 -226.9481 2.619997 172 11:49:15
## 2 -226.9481 2.619997 172 11:49:15
## 3 -226.9481 2.619997 172 11:49:15
## [1] "##############################"
## [1] "### Analyzing FHB in RAL19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:15
## LogLik Sigma2 DF wall
## 1 29.68725 1.0 169 11:49:15 ( 1 restrained)
## 2 30.95146 1.0 169 11:49:15 ( 1 restrained)
## 3 31.62021 1.0 169 11:49:15 ( 1 restrained)
## 4 31.71794 1.0 169 11:49:15 ( 1 restrained)
## 5 31.72481 1.0 169 11:49:15 ( 1 restrained)
## 6 31.72525 1.0 169 11:49:15
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 30.85
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 169 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 31.72525
##
## $nedf
## [1] 169
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 1.011929e-07 NA NA B NA
## units!R 1.000000e+00 NA NA F 0
##
## $bic
## [1] -58.32059
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -61.45049
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 30.85084
##
## $heterogeneity
## [1] 0.1825493
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:16
## LogLik Sigma2 DF wall
## 1 31.73035 1.0 169 11:49:16
## 2 31.72525 1.0 169 11:49:16
## 3 31.72525 1.0 169 11:49:16
## 4 31.72525 1.0 169 11:49:16
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 30.85
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 169 df.
## [1] "##############################"
## [1] "### Analyzing FDK in RAL19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:16
## LogLik Sigma2 DF wall
## 1 -394.6913 1.0 177 11:49:16 ( 1 restrained)
## 2 -393.8418 1.0 177 11:49:16 ( 1 restrained)
## 3 -394.5519 1.0 177 11:49:16
## 4 -394.1261 1.0 177 11:49:16
## 5 -393.8138 1.0 177 11:49:16
## 6 -393.6710 1.0 177 11:49:16
## 7 -393.6528 1.0 177 11:49:16
## 8 -393.6553 1.0 177 11:49:16
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 1415.37
## : Variance heterogenity factor (Deviance/df): 8.00 assuming 177 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -393.6553
##
## $nedf
## [1] 177
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.002464195 0.003662132 0.6728855 P 0.9
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] 792.4868
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 789.3107
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 1415.368
##
## $heterogeneity
## [1] 7.996429
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:17
## LogLik Sigma2 DF wall
## 1 -393.3045 1.0 177 11:49:17
## 2 -393.6558 1.0 177 11:49:17
## 3 -393.6558 1.0 177 11:49:17
## 4 -393.6558 1.0 177 11:49:17
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 1415.37
## : Variance heterogenity factor (Deviance/df): 8.00 assuming 177 df.
## [1] "##############################"
## [1] "### Analyzing DON in RAL19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:17
## LogLik Sigma2 DF wall
## 1 -329.2665 1.0 177 11:49:17
## 2 -374.8192 1.0 177 11:49:17
## 3 -376.6420 1.0 177 11:49:17
## 4 -376.6337 1.0 177 11:49:17
## 5 -376.6270 1.0 177 11:49:17
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 1252.01
## : Variance heterogenity factor (Deviance/df): 7.07 assuming 177 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -376.627
##
## $nedf
## [1] 177
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.03711523 0.05281526 0.7027369 P 0.2
## units!R 1.00000000 NA NA F 0.0
##
## $bic
## [1] 758.4302
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 755.254
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 1252.009
##
## $heterogeneity
## [1] 7.0735
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:18
## LogLik Sigma2 DF wall
## 1 -329.0394 1.0 177 11:49:18
## 2 -374.7773 1.0 177 11:49:18
## 3 -376.6273 1.0 177 11:49:18
## 4 -376.6273 1.0 177 11:49:18
## 5 -376.6273 1.0 177 11:49:18
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 1252.01
## : Variance heterogenity factor (Deviance/df): 7.07 assuming 177 df.
## [1] "#############################"
## [1] "### Analyzing PH in RAL19 ###"
## [1] "#############################"
## [1] "### PH not taken in RAL19 moving on..."
## [1] "#############################"
## [1] "### Analyzing HD in RAL20 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:18
## LogLik Sigma2 DF wall
## 1 -271.9577 2.940139 201 11:49:18
## 2 -271.9528 2.939200 201 11:49:18
## 3 -271.9477 2.937255 201 11:49:18
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## ASReml Version 4.2 05/07/2024 11:49:18
## LogLik Sigma2 DF wall
## 1 -271.9476 2.937007 201 11:49:18
## 2 -271.9476 2.937006 201 11:49:18
## 3 -271.9476 2.937003 201 11:49:18
## [1] "##############################"
## [1] "### Analyzing FHB in RAL20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:18
## LogLik Sigma2 DF wall
## 1 24.53280 1.0 202 11:49:18 ( 1 restrained)
## 2 25.76846 1.0 202 11:49:18 ( 1 restrained)
## 3 26.43301 1.0 202 11:49:18 ( 1 restrained)
## 4 26.52475 1.0 202 11:49:18 ( 1 restrained)
## 5 26.53106 1.0 202 11:49:18 ( 1 restrained)
## 6 26.53147 1.0 202 11:49:18
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 58.18
## : Variance heterogenity factor (Deviance/df): 0.29 assuming 202 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 26.53147
##
## $nedf
## [1] 202
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 1.011929e-07 NA NA B NA
## units!R 1.000000e+00 NA NA F 0
##
## $bic
## [1] -47.75467
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -51.06294
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 58.1805
##
## $heterogeneity
## [1] 0.2880223
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:19
## LogLik Sigma2 DF wall
## 1 26.56548 1.0 202 11:49:19
## 2 26.53147 1.0 202 11:49:19
## 3 26.53147 1.0 202 11:49:19
## 4 26.53147 1.0 202 11:49:19
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 58.18
## : Variance heterogenity factor (Deviance/df): 0.29 assuming 202 df.
## [1] "##############################"
## [1] "### Analyzing FDK in RAL20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:19
## LogLik Sigma2 DF wall
## 1 -214.0270 1.0 197 11:49:19 ( 1 restrained)
## 2 -214.9834 1.0 197 11:49:19
## 3 -214.9911 1.0 197 11:49:19
## 4 -214.9987 1.0 197 11:49:19
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 897.60
## : Variance heterogenity factor (Deviance/df): 4.56 assuming 197 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -214.9987
##
## $nedf
## [1] 197
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.007070315 0.01035858 0.6825567 P 0.3
## units!R 1.000000000 NA NA F 0.0
##
## $bic
## [1] 435.2806
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 431.9974
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 897.603
##
## $heterogeneity
## [1] 4.556361
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:20
## LogLik Sigma2 DF wall
## 1 -213.0443 1.0 197 11:49:20
## 2 -214.9989 1.0 197 11:49:20
## 3 -214.9992 1.0 197 11:49:20
## 4 -214.9992 1.0 197 11:49:20
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 897.60
## : Variance heterogenity factor (Deviance/df): 4.56 assuming 197 df.
## [1] "##############################"
## [1] "### Analyzing DON in RAL20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:20
## LogLik Sigma2 DF wall
## 1 -29.08515 1.0 93 11:49:20 ( 1 restrained)
## 2 -29.85891 1.0 93 11:49:20
## 3 -29.83334 1.0 93 11:49:20
## 4 -29.80953 1.0 93 11:49:20
## 5 -29.81208 1.0 93 11:49:20
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 99.30
## : Variance heterogenity factor (Deviance/df): 1.07 assuming 93 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -29.81208
##
## $nedf
## [1] 93
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.004069305 0.01078283 0.3773876 P 0
## units!R 1.000000000 NA NA F 0
##
## $bic
## [1] 64.15675
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 61.62415
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 99.29714
##
## $heterogeneity
## [1] 1.067711
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:21
## LogLik Sigma2 DF wall
## 1 -28.02039 1.0 93 11:49:21
## 2 -29.81076 1.0 93 11:49:21
## 3 -29.81165 1.0 93 11:49:21
## 4 -29.81210 1.0 93 11:49:21
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 99.30
## : Variance heterogenity factor (Deviance/df): 1.07 assuming 93 df.
## [1] "#############################"
## [1] "### Analyzing PH in RAL20 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:21
## LogLik Sigma2 DF wall
## 1 -416.1138 12.10252 202 11:49:21 ( 1 restrained)
## 2 -415.6330 12.17657 202 11:49:21
## 3 -415.6051 12.15841 202 11:49:21
## 4 -415.6035 12.15375 202 11:49:21
## ASReml Version 4.2 05/07/2024 11:49:21
## LogLik Sigma2 DF wall
## 1 -415.6035 12.15340 202 11:49:21
## 2 -415.6035 12.15340 202 11:49:21
## 3 -415.6035 12.15340 202 11:49:21
## [1] "#############################"
## [1] "### Analyzing HD in WAR19 ###"
## [1] "#############################"
## [1] "### HD not taken in WAR19 moving on..."
## [1] "##############################"
## [1] "### Analyzing FHB in WAR19 ###"
## [1] "##############################"
## [1] "### FHB not taken in WAR19 moving on..."
## [1] "##############################"
## [1] "### Analyzing FDK in WAR19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:21
## LogLik Sigma2 DF wall
## 1 -551.8200 1.0 174 11:49:22 ( 1 restrained)
## 2 -573.2883 1.0 174 11:49:22
## 3 -579.2964 1.0 174 11:49:22
## 4 -585.2846 1.0 174 11:49:22
## 5 -591.2799 1.0 174 11:49:22
## 6 -597.2778 1.0 174 11:49:22
## 7 -603.2767 1.0 174 11:49:22
## 8 -609.2762 1.0 174 11:49:22
## 9 -575.7600 1.0 174 11:49:22
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 10 12 -15.46 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 10 12 -16.46 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 11 12 -17.46 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, : Warning : Aborting GLM fit because predicted values are out of bounds
## : Try a more reasonable link function
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Error : Errors in GLM
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Log-likelihood not converged
## ASReml Version 4.2 05/07/2024 11:49:22
## LogLik Sigma2 DF wall
## 1 -627.6781 253.2274 174 11:49:22 ( 1 restrained)
## 2 -627.2055 254.9961 174 11:49:22
## 3 -627.1799 254.5715 174 11:49:22
## 4 -627.1784 254.4658 174 11:49:22
## ASReml Version 4.2 05/07/2024 11:49:22
## LogLik Sigma2 DF wall
## 1 -627.1784 254.4583 174 11:49:22
## 2 -627.1784 254.4583 174 11:49:22
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## G.param = fit$G.param, R.param = fit$R.param, data = pheno_subset,
## maxit = 75)
##
## $loglik
## [1] -627.1784
##
## $nedf
## [1] 174
##
## $sigma
## [1] 15.95175
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 2.756883 6.037772 0.4566061 P 0
## units!R 254.458308 27.359528 9.3005371 P 0
##
## $bic
## [1] 1264.675
## attr(,"parameters")
## [1] 2
##
## $aic
## [1] 1258.357
## attr(,"parameters")
## [1] 2
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:23
## LogLik Sigma2 DF wall
## 1 -627.1784 254.4583 174 11:49:23
## 2 -627.1784 254.4583 174 11:49:23
## 3 -627.1784 254.4583 174 11:49:23
## [1] "##############################"
## [1] "### Analyzing DON in WAR19 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:23
## LogLik Sigma2 DF wall
## 1 45.73517 1.0 175 11:49:23
## 2 40.30355 1.0 175 11:49:23
## 3 40.11113 1.0 175 11:49:23
## 4 40.11346 1.0 175 11:49:23
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 139.80
## : Variance heterogenity factor (Deviance/df): 0.80 assuming 175 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] 40.11346
##
## $nedf
## [1] 175
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.02672652 0.03442627 0.776341 P 7.7
## units!R 1.00000000 NA NA F 0.0
##
## $bic
## [1] -75.06213
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] -78.22691
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 139.7978
##
## $heterogeneity
## [1] 0.7988448
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 46.06474 1.0 175 11:49:24
## 2 40.32630 1.0 175 11:49:24
## 3 40.10768 1.0 175 11:49:24
## 4 40.10745 1.0 175 11:49:24
## 5 40.10745 1.0 175 11:49:24
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 139.79
## : Variance heterogenity factor (Deviance/df): 0.80 assuming 175 df.
## [1] "#############################"
## [1] "### Analyzing PH in WAR19 ###"
## [1] "#############################"
## [1] "### PH not taken in WAR19 moving on..."
## [1] "#############################"
## [1] "### Analyzing HD in WAR20 ###"
## [1] "#############################"
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 -229.2203 1.719027 213 11:49:24 ( 1 restrained)
## 2 -228.4299 1.724101 213 11:49:24
## 3 -228.4124 1.728254 213 11:49:24
## 4 -228.4026 1.726768 213 11:49:24
## 5 -228.4023 1.726514 213 11:49:24
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 -228.4023 1.726506 213 11:49:24
## 2 -228.4023 1.726506 213 11:49:24
## 3 -228.4023 1.726506 213 11:49:24
## [1] "##############################"
## [1] "### Analyzing FHB in WAR20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 -85.18770 1.0 212 11:49:24 ( 1 restrained)
## 2 -100.4930 1.0 212 11:49:24 ( 1 restrained)
## 3 -116.1241 1.0 212 11:49:24
## 4 -132.1610 1.0 212 11:49:24
## 5 -148.1610 1.0 212 11:49:24
## 6 -164.1611 1.0 212 11:49:24
## 7 -180.1611 1.0 212 11:49:24
## 8 -107.8146 1.0 212 11:49:24
## 9 -107.8147 1.0 212 11:49:24
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 181.59
## : Variance heterogenity factor (Deviance/df): 0.86 assuming 212 df.
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 8 34 -15.17 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 9 34 -16.17 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 9 34 -17.17 0 0.00
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Warning : Warning: GLM Bounds 10 34 -18.17 0 0.00
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 -215.0104 1.519328 212 11:49:24 ( 1 restrained)
## 2 -214.2430 1.524150 212 11:49:24
## 3 -214.2280 1.526795 212 11:49:24
## 4 -214.2247 1.526013 212 11:49:24
## Warning in asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units, :
## Some components changed by more than 1% on the last iteration
## ASReml Version 4.2 05/07/2024 11:49:24
## LogLik Sigma2 DF wall
## 1 -214.2247 1.525930 212 11:49:24
## 2 -214.2247 1.525930 212 11:49:24
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## G.param = fit$G.param, R.param = fit$R.param, data = pheno_subset,
## maxit = 75)
##
## $loglik
## [1] -214.2247
##
## $nedf
## [1] 212
##
## $sigma
## [1] 1.235285
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.005279045 0.01883725 0.2802451 P 0
## units!R 1.525929805 0.14856223 10.2713171 P 0
##
## $bic
## [1] 439.1625
## attr(,"parameters")
## [1] 2
##
## $aic
## [1] 432.4494
## attr(,"parameters")
## [1] 2
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:25
## LogLik Sigma2 DF wall
## 1 -214.2247 1.525929 212 11:49:25
## 2 -214.2247 1.525929 212 11:49:25
## 3 -214.2247 1.525929 212 11:49:25
## [1] "##############################"
## [1] "### Analyzing FDK in WAR20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:25
## LogLik Sigma2 DF wall
## 1 -130.7596 1.0 207 11:49:25 ( 1 restrained)
## 2 -130.1646 1.0 207 11:49:25 ( 1 restrained)
## 3 -129.2463 1.0 207 11:49:25
## 4 -129.2151 1.0 207 11:49:25
## 5 -129.2211 1.0 207 11:49:25
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 741.94
## : Variance heterogenity factor (Deviance/df): 3.58 assuming 207 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -129.2211
##
## $nedf
## [1] 207
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 0.0002927804 0.0007981389 0.3668289 P 0.3
## units!R 1.0000000000 NA NA F 0.0
##
## $bic
## [1] 263.7748
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 260.4421
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 741.9373
##
## $heterogeneity
## [1] 3.584238
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:26
## LogLik Sigma2 DF wall
## 1 -128.4636 1.0 207 11:49:26
## 2 -129.2211 1.0 207 11:49:26
## 3 -129.2212 1.0 207 11:49:26
## 4 -129.2213 1.0 207 11:49:26
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 741.94
## : Variance heterogenity factor (Deviance/df): 3.58 assuming 207 df.
## [1] "##############################"
## [1] "### Analyzing DON in WAR20 ###"
## [1] "##############################"
## ASReml Version 4.2 05/07/2024 11:49:26
## LogLik Sigma2 DF wall
## 1 -17.95262 1.0 205 11:49:26 ( 1 restrained)
## 2 -17.91268 1.0 205 11:49:26 ( 1 restrained)
## 3 -17.44163 1.0 205 11:49:26 ( 1 restrained)
## 4 -17.41066 1.0 205 11:49:26 ( 1 restrained)
## 5 -17.40858 1.0 205 11:49:26 ( 1 restrained)
## 6 -17.40842 1.0 205 11:49:26
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 36.32
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 205 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP, residual = ~units,
## family = asr_poisson(), data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -17.40842
##
## $nedf
## [1] 205
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP 1.011929e-07 NA NA B NA
## units!R 1.000000e+00 NA NA F 0
##
## $bic
## [1] 40.13984
## attr(,"parameters")
## [1] 1
##
## $aic
## [1] 36.81683
## attr(,"parameters")
## [1] 1
##
## $deviance
## [1] 36.32193
##
## $heterogeneity
## [1] 0.1771801
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:27
## LogLik Sigma2 DF wall
## 1 -16.21597 1.0 205 11:49:27
## 2 -17.40785 1.0 205 11:49:27
## 3 -17.40842 1.0 205 11:49:27
## 4 -17.40842 1.0 205 11:49:27
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 36.32
## : Variance heterogenity factor (Deviance/df): 0.18 assuming 205 df.
## [1] "#############################"
## [1] "### Analyzing PH in WAR20 ###"
## [1] "#############################"
## [1] "### PH not taken in WAR20 moving on..."
Multi-environmental models
# Define traits
traits <- colnames(pheno)[8:12]
# Create dataframe for predictions
BLUEs_mem <- data.frame(GENOTYPE = unique(pheno$GENOTYPE))
# Run MEMLM loop for each trait
for (i in traits) {
# Print message
print_message <- paste("### Analyzing", i, "###")
print(paste(rep("#", nchar(print_message)), collapse = ""))
print(print_message)
print(paste(rep("#", nchar(print_message)), collapse = ""))
# Subset data
pheno_subset <- na.omit(pheno[, c("GENOTYPE", "YEAR", "ENV", "REP", i)])
# Redefine data types
pheno_subset[, 1:4] <- lapply(pheno_subset[, 1:4], as.factor)
pheno_subset[, 5] <- as.numeric(pheno_subset[, 5])
colnames(pheno_subset)[5] <- "Y"
# Run mixed effects model
if (i %in% c("FHB", "FDK", "DON")) {
# Fit model with Poisson distribution
fit <- asreml(fixed = Y ~ GENOTYPE,
random = ~ REP:ENV + GENOTYPE:ENV,
residual = ~ units,
data = pheno_subset,
family = asr_poisson(),
maxit = 75)
} else {
# Fit model with normal distribution
fit <- asreml(fixed = Y ~ GENOTYPE,
random = ~ REP:ENV + GENOTYPE:ENV,
residual = ~ units,
data = pheno_subset,
#family = asr_poisson(),
maxit = 75)
}
# Get quick summary
print(summary(fit))
print(plot(fit))
# Get predictions
pred <- predict(fit, classify = "GENOTYPE")$pvals[, 1:2]
colnames(pred)[2] <- paste(i, "ME", sep = "_")
# Left join predictions to main dataframe
BLUEs_mem <- left_join(BLUEs_mem, pred, by = "GENOTYPE")
# Remove temporary objects
remove(pheno_subset, fit, pred)
}
## [1] "####################"
## [1] "### Analyzing HD ###"
## [1] "####################"
## ASReml Version 4.2 05/07/2024 11:49:34
## LogLik Sigma2 DF wall
## 1 -2589.554 13.45284 1305 11:49:34
## 2 -2187.277 6.566912 1305 11:49:34
## 3 -1891.054 4.237486 1305 11:49:34
## 4 -1736.569 3.279107 1305 11:49:34
## 5 -1650.794 2.874223 1305 11:49:34
## 6 -1613.385 2.697494 1305 11:49:34
## 7 -1596.627 2.620326 1305 11:49:34
## 8 -1589.474 2.582799 1305 11:49:34
## 9 -1586.795 2.564885 1305 11:49:34
## 10 -1586.047 2.556611 1305 11:49:34
## 11 -1585.938 2.553459 1305 11:49:34
## 12 -1585.934 2.552805 1305 11:49:34
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP:ENV + GENOTYPE:ENV,
## residual = ~units, data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -1585.934
##
## $nedf
## [1] 1305
##
## $sigma
## [1] 1.59775
##
## $varcomp
## component std.error z.ratio bound %ch
## REP:ENV 212.1094295 113.0140989 1.87684 P 0.2
## GENOTYPE:ENV 0.6367875 0.1232248 5.16769 P 0.0
## units!R 2.5528045 0.1241995 20.55407 P 0.0
##
## $bic
## [1] 3193.389
## attr(,"parameters")
## [1] 3
##
## $aic
## [1] 3177.867
## attr(,"parameters")
## [1] 3
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:35
## LogLik Sigma2 DF wall
## 1 -1585.934 2.552773 1305 11:49:35
## 2 -1585.934 2.552773 1305 11:49:35
## 3 -1585.934 2.552773 1305 11:49:35
## [1] "#####################"
## [1] "### Analyzing FHB ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:35
## LogLik Sigma2 DF wall
## 1 235.7098 1.0 1599 11:49:35 ( 1 restrained)
## 2 341.8815 1.0 1599 11:49:35 ( 1 restrained)
## 3 342.0703 1.0 1599 11:49:35 ( 1 restrained)
## 4 342.6015 1.0 1599 11:49:35 ( 1 restrained)
## 5 342.5865 1.0 1599 11:49:35 ( 1 restrained)
## 6 342.5875 1.0 1599 11:49:35
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 822.29
## : Variance heterogenity factor (Deviance/df): 0.51 assuming 1599 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP:ENV + GENOTYPE:ENV,
## residual = ~units, family = asr_poisson(), data = pheno_subset,
## maxit = 75)
##
## $loglik
## [1] 342.5875
##
## $nedf
## [1] 1599
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP:ENV 5.653994e-02 0.02751384 2.054963 P 0
## GENOTYPE:ENV 1.011929e-07 NA NA B NA
## units!R 1.000000e+00 NA NA F 0
##
## $bic
## [1] -670.4208
## attr(,"parameters")
## [1] 2
##
## $aic
## [1] -681.1751
## attr(,"parameters")
## [1] 2
##
## $deviance
## [1] 822.2872
##
## $heterogeneity
## [1] 0.5142509
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:36
## LogLik Sigma2 DF wall
## 1 342.6995 1.0 1599 11:49:36
## 2 342.5876 1.0 1599 11:49:36
## 3 342.5876 1.0 1599 11:49:36
## 4 342.5876 1.0 1599 11:49:36
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 822.29
## : Variance heterogenity factor (Deviance/df): 0.51 assuming 1599 df.
## [1] "#####################"
## [1] "### Analyzing FDK ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:36
## LogLik Sigma2 DF wall
## 1 -1422.182 1.0 1947 11:49:37
## 2 -1428.506 1.0 1947 11:49:37
## 3 -1427.713 1.0 1947 11:49:37
## 4 -1429.077 1.0 1947 11:49:37
## 5 -1430.580 1.0 1947 11:49:37
## 6 -1431.569 1.0 1947 11:49:37
## 7 -1432.232 1.0 1947 11:49:37
## 8 -1432.680 1.0 1947 11:49:37
## 9 -1433.522 1.0 1947 11:49:37
## 10 -1433.561 1.0 1947 11:49:37
## 11 -1433.600 1.0 1947 11:49:37
## 12 -1433.602 1.0 1947 11:49:37
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 6741.13
## : Variance heterogenity factor (Deviance/df): 3.46 assuming 1947 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP:ENV + GENOTYPE:ENV,
## residual = ~units, family = asr_poisson(), data = pheno_subset,
## maxit = 75)
##
## $loglik
## [1] -1433.602
##
## $nedf
## [1] 1947
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP:ENV 0.1091502 0.047187540 2.313116 P 0
## GENOTYPE:ENV 0.1470754 0.008583812 17.134038 P 0
## units!R 1.0000000 NA NA F 0
##
## $bic
## [1] 2882.353
## attr(,"parameters")
## [1] 2
##
## $aic
## [1] 2871.205
## attr(,"parameters")
## [1] 2
##
## $deviance
## [1] 6741.135
##
## $heterogeneity
## [1] 3.462319
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:38
## LogLik Sigma2 DF wall
## 1 -1421.484 1.0 1947 11:49:38
## 2 -1433.587 1.0 1947 11:49:38
## 3 -1433.595 1.0 1947 11:49:38
## 4 -1433.602 1.0 1947 11:49:38
## 5 -1433.602 1.0 1947 11:49:38
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 6741.13
## : Variance heterogenity factor (Deviance/df): 3.46 assuming 1947 df.
## [1] "#####################"
## [1] "### Analyzing DON ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:38
## LogLik Sigma2 DF wall
## 1 -289.2838 1.0 1790 11:49:38
## 2 -284.0212 1.0 1790 11:49:38
## 3 -329.5288 1.0 1790 11:49:38
## 4 -353.0202 1.0 1790 11:49:38
## 5 -351.9304 1.0 1790 11:49:38
## 6 -350.6739 1.0 1790 11:49:38
## 7 -350.3405 1.0 1790 11:49:38
## 8 -350.3911 1.0 1790 11:49:38
## 9 -350.4227 1.0 1790 11:49:38
## 10 -350.4238 1.0 1790 11:49:38
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 2681.43
## : Variance heterogenity factor (Deviance/df): 1.50 assuming 1790 df.
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP:ENV + GENOTYPE:ENV,
## residual = ~units, family = asr_poisson(), data = pheno_subset,
## maxit = 75)
##
## $loglik
## [1] -350.4238
##
## $nedf
## [1] 1790
##
## $sigma
## [1] 1
##
## $varcomp
## component std.error z.ratio bound %ch
## REP:ENV 0.63966663 0.273794319 2.336303 P 0
## GENOTYPE:ENV 0.08798913 0.007822804 11.247773 P 0
## units!R 1.00000000 NA NA F 0
##
## $bic
## [1] 715.8275
## attr(,"parameters")
## [1] 2
##
## $aic
## [1] 704.8475
## attr(,"parameters")
## [1] 2
##
## $deviance
## [1] 2681.432
##
## $heterogeneity
## [1] 1.498007
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:39
## LogLik Sigma2 DF wall
## 1 -278.0900 1.0 1790 11:49:39
## 2 -272.9965 1.0 1790 11:49:39
## 3 -326.0900 1.0 1790 11:49:39
## 4 -350.7465 1.0 1790 11:49:39
## 5 -350.8672 1.0 1790 11:49:39
## 6 -350.5430 1.0 1790 11:49:39
## 7 -350.4159 1.0 1790 11:49:40
## 8 -350.4242 1.0 1790 11:49:40
## 9 -350.4237 1.0 1790 11:49:40
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 2681.43
## : Variance heterogenity factor (Deviance/df): 1.50 assuming 1790 df.
## [1] "####################"
## [1] "### Analyzing PH ###"
## [1] "####################"
## ASReml Version 4.2 05/07/2024 11:49:40
## LogLik Sigma2 DF wall
## 1 -1211.680 14.08651 586 11:49:40
## 2 -1210.549 13.74732 586 11:49:40
## 3 -1209.640 13.33277 586 11:49:40
## 4 -1209.358 12.98586 586 11:49:40
## 5 -1209.357 12.96550 586 11:49:40
## $call
## asreml(fixed = Y ~ GENOTYPE, random = ~REP:ENV + GENOTYPE:ENV,
## residual = ~units, data = pheno_subset, maxit = 75)
##
## $loglik
## [1] -1209.357
##
## $nedf
## [1] 586
##
## $sigma
## [1] 3.600764
##
## $varcomp
## component std.error z.ratio bound %ch
## REP:ENV 1.476929 1.2864625 1.148054 P 0.1
## GENOTYPE:ENV 3.488914 1.1138624 3.132267 P 0.0
## units!R 12.965500 0.8947512 14.490620 P 0.0
##
## $bic
## [1] 2437.834
## attr(,"parameters")
## [1] 3
##
## $aic
## [1] 2424.714
## attr(,"parameters")
## [1] 3
##
## attr(,"class")
## [1] "summary.asreml"
## $histogram
##
## $qq
##
## $fitted
##
## $rowlabels
##
## ASReml Version 4.2 05/07/2024 11:49:41
## LogLik Sigma2 DF wall
## 1 -1209.357 12.96567 586 11:49:41
## 2 -1209.357 12.96567 586 11:49:41
## 3 -1209.357 12.96567 586 11:49:41
# Select specific traits from the results
BLUEs_mem <- BLUEs_mem %>%
select(GENOTYPE, FHB_ME, FDK_ME, DON_ME, HD_ME, PH_ME)
Heritabilities
# Make an empty data frame for the heritability estimates
h2s <- data.frame(Trait = character(),
Type = character(),
Estimation = numeric(),
SE = numeric())
# Calculate the harmonic means for each trait
e_traits <- list()
r_traits <- list()
for (j in traits) {
# Subset data and only take complete case
a <- pheno %>%
drop_na(all_of(j))
# Get the number of observations per genotype
obs_per_ind <- aggregate(a[, j] ~ GENOTYPE,
data = a,
length)
# Calculate the harmonic mean of replications
r <- (1 / mean(1 / obs_per_ind$`a[, j]`))
# Put that in an object
r_traits[j] <- r
# Create a new object called e
e <- c()
# For every genotype in the phenotypic dataset
for (i in unique(pheno$GENOTYPE)) {
# Get every observation in every environment
a <- na.omit(pheno[pheno$GENOTYPE == i, c("ENV", j)])
# Get get the number of environments that the genotype was observed in
a <- length(unique(a$ENV))
# If the number of environments equals 0, then 0, if it is greater than that, do 1 / number of environments
a <- ifelse(a == 0, 0, 1 / a)
# Add this value to the vector e
e <- rbind(e, a)
}
# Take the reciprocal of the value e to get the harmonic mean of environments
e_traits[j] <- (1 / mean(e))
# Remove all the little things
remove(a, obs_per_ind, r, e)
}
# Loop through each trait for heritability estimation
for (i in traits) {
# Print message
print_message <- paste("### Analyzing", i, "###")
print(paste(rep("#", nchar(print_message)), collapse = ""))
print(print_message)
print(paste(rep("#", nchar(print_message)), collapse = ""))
# Determine number of locations
n_loc <- pheno %>%
select(ENV, YEAR, LOC, all_of(i)) %>%
drop_na() %>%
distinct(ENV)
n_loc <- as.numeric(nrow(n_loc))
# Determine the number of replications
n_rep <- n_loc * 2
# Determin the number of years
n_year <- pheno %>%
select(ENV, YEAR, LOC, all_of(i)) %>%
drop_na() %>%
distinct(YEAR)
n_year <- as.numeric(count(n_year))
# Get the harmonic mean of environments
e <- as.numeric(e_traits[i])
# Get the harmonic mean of replications
r <- as.numeric(r_traits[i])
# If the trait has been observed over many years
if (n_year >= 2) {
# If the trait is a disease reaction trait
if (i %in% c("FHB", "FDK", "DON")) {
# Run model for Poisson distributed traits
mlm <- asreml(fixed = pheno[, i] ~ 1,
random = ~ GENOTYPE +
ENV +
GENOTYPE:ENV,
residual = ~ idv(units),
data = pheno,
maxit = 75,
family = asr_poisson())
} else {
# Run model for normally distributed traits
mlm <- asreml(fixed = pheno[, i] ~ 1,
random = ~ (GENOTYPE) +
ENV +
GENOTYPE:ENV,
residual = ~ idv(units),
data = pheno,
maxit = 75)
}
# If the trait has been only observed in one year
} else {
# Run model for single year data
mlm <- asreml(fixed = pheno[, i] ~ 1,
random = ~ (GENOTYPE) +
ENV +
GENOTYPE:ENV,
residual = ~ idv(units),
data = pheno,
maxit = 75)
}
# Print summary of variance components
print(summary(mlm)$varcomp)
# Predict heritability estimates
pph2 <- vpredict(mlm,
h2 ~ V2 / (V2 + V3 + V4))
emh2 <- vpredict(mlm,
h2 ~ V2 / (V2 + (V3 / e) + (V4 / (e * r))))
# Bind predictions to h2s data frame
a <- rbind(pph2, emh2)
a <- cbind(data.frame(Trait = c(i, i)), data.frame(Type = c("Per-Plot", "Entry-Mean")), a)
h2s <- rbind(h2s, a)
# Remove the little things
remove(mlm, pph2, emh2, n_loc, n_rep, n_year, e, r, a)
}
## [1] "####################"
## [1] "### Analyzing HD ###"
## [1] "####################"
## ASReml Version 4.2 05/07/2024 11:49:44
## LogLik Sigma2 DF wall
## 1 -2405.247 1.0 1480 11:49:44 ( 1 restrained)
## 2 -2348.740 1.0 1480 11:49:44
## 3 -2066.589 1.0 1480 11:49:44
## 4 -1834.873 1.0 1480 11:49:44
## 5 -1806.601 1.0 1480 11:49:44
## 6 -1798.658 1.0 1480 11:49:44
## 7 -1794.992 1.0 1480 11:49:44
## 8 -1793.582 1.0 1480 11:49:44
## 9 -1793.153 1.0 1480 11:49:44
## 10 -1793.078 1.0 1480 11:49:44
## 11 -1793.074 1.0 1480 11:49:44
## Warning in asreml(fixed = pheno[, i] ~ 1, random = ~(GENOTYPE) + ENV +
## GENOTYPE:ENV, : Warning : Log-likelihood decreased to -2713.75; trying with
## reduced updates 0.936318
## component std.error z.ratio bound %ch
## ENV 247.4503074 200.5523769 1.233844 P 0.5
## GENOTYPE 3.4636787 0.4237671 8.173543 P 0.0
## GENOTYPE:ENV 0.6107864 0.1235586 4.943295 P 0.0
## units!units 2.6341461 0.1273212 20.688983 P 0.0
## units!R 1.0000000 NA NA F 0.0
## [1] "#####################"
## [1] "### Analyzing FHB ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:44
## LogLik Sigma2 DF wall
## 1 326.3373 1.0 1773 11:49:44
## 2 446.0406 1.0 1773 11:49:44
## 3 574.2352 1.0 1773 11:49:44
## 4 644.9267 1.0 1773 11:49:44
## 5 667.3598 1.0 1773 11:49:44
## 6 667.7294 1.0 1773 11:49:44
## 7 667.7791 1.0 1773 11:49:44
## 8 667.7792 1.0 1773 11:49:44
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 657.12
## : Variance heterogenity factor (Deviance/df): 0.37 assuming 1773 df.
## component std.error z.ratio bound %ch
## ENV 0.07142689 0.050885047 1.403691 P 0
## GENOTYPE 0.09048161 0.011620599 7.786312 P 0
## GENOTYPE:ENV 0.02267221 0.005020015 4.516362 P 0
## units!units 0.38850437 0.017654202 22.006341 P 0
## units!R 1.00000000 NA NA F 0
## [1] "#####################"
## [1] "### Analyzing FDK ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:44
## LogLik Sigma2 DF wall
## 1 -275.6635 1.0 2121 11:49:44 ( 1 restrained)
## 2 -1340.498 1.0 2121 11:49:44
## 3 -792.0497 1.0 2121 11:49:44
## 4 -200.8308 1.0 2121 11:49:44
## 5 141.1055 1.0 2121 11:49:44
## 6 268.4041 1.0 2121 11:49:44
## 7 283.0714 1.0 2121 11:49:44
## 8 283.3257 1.0 2121 11:49:44
## 9 283.2893 1.0 2121 11:49:44
## 10 283.2885 1.0 2121 11:49:44
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 10288.48
## : Variance heterogenity factor (Deviance/df): 4.85 assuming 2121 df.
## component std.error z.ratio bound %ch
## ENV 0.10982711 0.070001196 1.568932 P 0
## GENOTYPE 0.13666405 0.017265438 7.915470 P 0
## GENOTYPE:ENV 0.03682012 0.007001431 5.258942 P 0
## units!units 5.32960798 0.217433176 24.511476 P 0
## units!R 1.00000000 NA NA F 0
## [1] "#####################"
## [1] "### Analyzing DON ###"
## [1] "#####################"
## ASReml Version 4.2 05/07/2024 11:49:44
## LogLik Sigma2 DF wall
## 1 -651.6594 1.0 1964 11:49:44 ( 1 restrained)
## 2 -1064.670 1.0 1964 11:49:44
## 3 -815.7820 1.0 1964 11:49:44
## 4 -454.3617 1.0 1964 11:49:44
## 5 -254.4660 1.0 1964 11:49:44
## 6 -187.1139 1.0 1964 11:49:44
## 7 -181.8392 1.0 1964 11:49:44
## 8 -181.7502 1.0 1964 11:49:44
## 9 -181.7521 1.0 1964 11:49:44
## Notice : Distribution and link: Poisson; Log Mu=exp(XB) V=Mu
## Notice : The LogL value is unsuitable for comparing GLM models
## Notice : Deviance from GLM fit: 3421.58
## : Variance heterogenity factor (Deviance/df): 1.74 assuming 1964 df.
## component std.error z.ratio bound %ch
## ENV 0.71456770 0.453102633 1.577055 P 0
## GENOTYPE 0.21816468 0.027280611 7.997060 P 0
## GENOTYPE:ENV 0.05330835 0.008306883 6.417371 P 0
## units!units 2.00383964 0.079312497 25.265119 P 0
## units!R 1.00000000 NA NA F 0
## [1] "####################"
## [1] "### Analyzing PH ###"
## [1] "####################"
## ASReml Version 4.2 05/07/2024 11:49:44
## LogLik Sigma2 DF wall
## 1 -2712.686 1.0 758 11:49:45
## 2 -2359.612 1.0 758 11:49:45
## 3 -1980.620 1.0 758 11:49:45
## 4 -1735.077 1.0 758 11:49:45
## 5 -1621.030 1.0 758 11:49:45
## 6 -1597.801 1.0 758 11:49:45
## 7 -1595.828 1.0 758 11:49:45
## 8 -1595.795 1.0 758 11:49:45
## 9 -1595.795 1.0 758 11:49:45
## component std.error z.ratio bound %ch
## ENV 2.231889 3.2369574 0.6895022 P 0
## GENOTYPE 27.283118 3.5190437 7.7529922 P 0
## GENOTYPE:ENV 3.444573 1.1154276 3.0881190 P 0
## units!units 13.065242 0.8994521 14.5257788 P 0
## units!R 1.000000 NA NA F 0
Make the rQTL file
# Read in genetic information from VCF file
geno <- read.vcf("NC13-20076xGA06493-13LE6_filt.vcf.gz", convert.chr = FALSE)
## ped stats and snps stats have been set.
## 'p' has been set.
## 'mu' and 'sigma' have been set.
# Convert genotype matrix to format suitable for rqtl
geno <- format_qtlmap_geno(geno,
par_a = "13955-GA06493-13LE6",
par_b = "13955-NC13-20076",
rm_het = TRUE,
rm_miss = TRUE,
include_pars = TRUE,
out_fmt = "rqtl")$abh
# Replace heterozygous calls with missing data
geno <- replace(geno, geno == "H", "-")
# Manipulate the genetic file to match rqtl's format requirements
geno <- as.data.frame(t(geno))
colnames(geno) <- geno[1, ]
geno <- geno[-1, ]
geno <- cbind(substr(gsub("[.]", "-", rownames(geno)), start = 2, stop = nchar(rownames(geno))), geno)
rownames(geno) <- NULL
geno <- rownames_to_column(geno, var = "dummy") # Create a dummy column to sort later on
geno$dummy <- as.numeric(geno$dummy)
colnames(geno)[2] <- "GENOTYPE"
geno[1:2, 2] <- ""
# Merge all the BLUEs
BLUEs_all <- left_join(BLUEs_mem, BLUEs_loc, by = "GENOTYPE")
# Merge the phenotype and genotype files
insertme <- full_join(BLUEs_all, geno, by = "GENOTYPE")
insertme <- insertme[order(insertme$dummy), ] # Order by dummy
insertme <- insertme[1, ]
pheno_geno <- left_join(BLUEs_all, geno, by = "GENOTYPE")
pheno_geno <- rbind(insertme, pheno_geno)
pheno_geno <- pheno_geno[order(pheno_geno$dummy), ] # Order by dummy
pheno_geno <- pheno_geno %>% filter(!is.na(dummy)) # Get rid of any genotype that is not present
pheno_geno[1, (1:30)] <- "" # Replace any NAs with blank data
dropme <- c("13955-AGS-2026", "13955-JAMESTOWN", "13955-NCAG11", "13955-NC13-20076", "13955-GA06493-13LE6")
pheno_geno <- pheno_geno[!(pheno_geno$GENOTYPE %in% dropme), ]
pheno_geno <- pheno_geno %>%
select(-dummy)
colnames(pheno_geno) <- gsub("FHB", "VR", colnames(pheno_geno))
# Write out rqtl input file
write.csv(pheno_geno,
"rqtl_input_file.csv",
row.names = FALSE)
Make a pairs plot of traits
# Create temp
pairs_plot_dat<-BLUEs_mem
# Rename columns for the pairs plot
colnames(pairs_plot_dat) <- c("Genotype",
"Visual Rating (1-9)",
"Fusarium Damaged Kernels (%)",
"Deoxynivalenol Content (PPM)",
"Heading Date (Days)",
"Plant Height (cm)")
# Visualize first
pairs.panels(pairs_plot_dat[,2:ncol(pairs_plot_dat)],
hist.col = "gray",
lm = TRUE,
stars = TRUE,
digits = 2,
density = FALSE,
ellipses = FALSE)
# Set up JPEG file
jpeg(filename = "pairs_plot.jpg",
width = 9,
height = 9,
units = "in",
res = 320)
# Create pairs plot
pairs.panels(pairs_plot_dat[,2:ncol(pairs_plot_dat)],
hist.col = "gray",
lm = TRUE,
stars = TRUE,
digits = 2,
density = FALSE,
ellipses = FALSE)
# Save and close JPEG file
dev.off()
## png
## 2
Build Linkage Map
# Read in file
cross_file <- read.cross("csv",
file = "rqtl_input_file.csv",
genotypes = c("A", "B", "-"),
alleles = c("A", "B"),
crosstype = "dh")
## --Read the following data:
## 166 individuals
## 2954 markers
## 29 phenotypes
## Warning in summary.cross(cross): Some chromosomes > 1000 cM in length; there may be a problem with the genetic map.
## (Perhaps it is in basepairs?)
## --Cross type: dh
# Remove markers with low P-value from a file
toss_me <- read.table("markers_to_remove.txt")[, 1]
gt <- geno.table(cross_file)
toss_me <- unique(c(toss_me, rownames(gt[gt$P.value < 0.0001, ])))
cross_file <- drop.markers(cross_file, toss_me)
# Remove unnecessary objects
remove(gt, toss_me)
# Chromosome names
chromosomes <- c(paste0("1", LETTERS[c(1,2,4)]),
paste0("2", LETTERS[c(1,2,4)]),
paste0("3", LETTERS[c(1,2,4)]),
paste0("4", LETTERS[c(1,2,4)]),
paste0("5", LETTERS[c(1,2,4)]),
paste0("6", LETTERS[c(1,2,4)]),
paste0("7", LETTERS[c(1,2,4)]))
# Create genetic map with cM instead of BP
cross_file <- mstmap(cross_file,
pop.type = "dh",
id = "GENOTYPE",
chr = chromosomes,
anchor = TRUE,
detectBadData = TRUE,
bychr = TRUE,
miss.thresh = 0.15,
mvest.bc = FALSE)
## caution! marker: S1A_15034885 was killed due to too many missing genotype calls
## caution! marker: S1A_15345816 was killed due to too many missing genotype calls
## caution! marker: S1A_16022986 was killed due to too many missing genotype calls
## caution! marker: S1A_275765978 was killed due to too many missing genotype calls
## caution! marker: S1A_294513533 was killed due to too many missing genotype calls
## caution! marker: S1A_294735020 was killed due to too many missing genotype calls
## caution! marker: S1A_297381169 was killed due to too many missing genotype calls
## caution! marker: S1A_3618173 was killed due to too many missing genotype calls
## caution! marker: S1A_37507495 was killed due to too many missing genotype calls
## caution! marker: S1A_42151448 was killed due to too many missing genotype calls
## caution! marker: S1A_46222311 was killed due to too many missing genotype calls
## caution! marker: S1A_48769407 was killed due to too many missing genotype calls
## caution! marker: S1A_48786251 was killed due to too many missing genotype calls
## caution! marker: S1A_49245446 was killed due to too many missing genotype calls
## caution! marker: S1A_497499071 was killed due to too many missing genotype calls
## caution! marker: S1A_498539597 was killed due to too many missing genotype calls
## caution! marker: S1A_499808950 was killed due to too many missing genotype calls
## caution! marker: S1A_499864420 was killed due to too many missing genotype calls
## caution! marker: S1A_509676136 was killed due to too many missing genotype calls
## caution! marker: S1A_509676242 was killed due to too many missing genotype calls
## caution! marker: S1A_510292911 was killed due to too many missing genotype calls
## caution! marker: S1A_510983454 was killed due to too many missing genotype calls
## caution! marker: S1A_511486283 was killed due to too many missing genotype calls
## caution! marker: S1A_511633074 was killed due to too many missing genotype calls
## caution! marker: S1A_513896120 was killed due to too many missing genotype calls
## caution! marker: S1A_516064856 was killed due to too many missing genotype calls
## caution! marker: S1A_519799657 was killed due to too many missing genotype calls
## caution! marker: S1A_531610222 was killed due to too many missing genotype calls
## caution! marker: S1A_531924600 was killed due to too many missing genotype calls
## caution! marker: S1A_532489535 was killed due to too many missing genotype calls
## caution! marker: S1A_534272127 was killed due to too many missing genotype calls
## caution! marker: S1A_535408302 was killed due to too many missing genotype calls
## caution! marker: S1A_535858090 was killed due to too many missing genotype calls
## caution! marker: S1A_535882220 was killed due to too many missing genotype calls
## caution! marker: S1A_540716970 was killed due to too many missing genotype calls
## caution! marker: S1A_541320369 was killed due to too many missing genotype calls
## caution! marker: S1A_541992725 was killed due to too many missing genotype calls
## caution! marker: S1A_543386586 was killed due to too many missing genotype calls
## caution! marker: S1A_547004982 was killed due to too many missing genotype calls
## caution! marker: S1A_581203166 was killed due to too many missing genotype calls
## caution! marker: S1A_7214024 was killed due to too many missing genotype calls
## caution! marker: S1A_8704388 was killed due to too many missing genotype calls
## caution! marker: S1A_9236518 was killed due to too many missing genotype calls
## caution! marker: S1A_9954029 was killed due to too many missing genotype calls
## Number of linkage groups: 3
## The size of the linkage groups are: 89 11 1
## The number of bins in each linkage group: 47 7 1
## caution! marker: S1B_15099360 was killed due to too many missing genotype calls
## caution! marker: S1B_333781271 was killed due to too many missing genotype calls
## caution! marker: S1B_340462171 was killed due to too many missing genotype calls
## caution! marker: S1B_340462230 was killed due to too many missing genotype calls
## caution! marker: S1B_491229424 was killed due to too many missing genotype calls
## caution! marker: S1B_505061917 was killed due to too many missing genotype calls
## caution! marker: S1B_555776982 was killed due to too many missing genotype calls
## caution! marker: S1B_556986919 was killed due to too many missing genotype calls
## caution! marker: S1B_562303166 was killed due to too many missing genotype calls
## caution! marker: S1B_564637532 was killed due to too many missing genotype calls
## caution! marker: S1B_564859110 was killed due to too many missing genotype calls
## caution! marker: S1B_577038108 was killed due to too many missing genotype calls
## caution! marker: S1B_581199980 was killed due to too many missing genotype calls
## caution! marker: S1B_582108393 was killed due to too many missing genotype calls
## caution! marker: S1B_583759748 was killed due to too many missing genotype calls
## caution! marker: S1B_5867248 was killed due to too many missing genotype calls
## caution! marker: S1B_598697932 was killed due to too many missing genotype calls
## caution! marker: S1B_601415609 was killed due to too many missing genotype calls
## caution! marker: S1B_621266693 was killed due to too many missing genotype calls
## caution! marker: S1B_630654556 was killed due to too many missing genotype calls
## caution! marker: S1B_630930383 was killed due to too many missing genotype calls
## caution! marker: S1B_637594410 was killed due to too many missing genotype calls
## caution! marker: S1B_640613228 was killed due to too many missing genotype calls
## caution! marker: S1B_643177495 was killed due to too many missing genotype calls
## caution! marker: S1B_645623521 was killed due to too many missing genotype calls
## caution! marker: S1B_649186897 was killed due to too many missing genotype calls
## caution! marker: S1B_6576639 was killed due to too many missing genotype calls
## caution! marker: S1B_662202259 was killed due to too many missing genotype calls
## caution! marker: S1B_671563195 was killed due to too many missing genotype calls
## caution! marker: S1B_672364315 was killed due to too many missing genotype calls
## caution! marker: S1B_673593968 was killed due to too many missing genotype calls
## caution! marker: S1B_673889393 was killed due to too many missing genotype calls
## caution! marker: S1B_679383883 was killed due to too many missing genotype calls
## caution! marker: S1B_681834051 was killed due to too many missing genotype calls
## caution! marker: S1B_683415189 was killed due to too many missing genotype calls
## caution! marker: S1B_683589393 was killed due to too many missing genotype calls
## caution! marker: S1B_686876645 was killed due to too many missing genotype calls
## caution! marker: S1B_687334531 was killed due to too many missing genotype calls
## caution! marker: S1B_687536156 was killed due to too many missing genotype calls
## caution! marker: S1B_687785710 was killed due to too many missing genotype calls
## Number of linkage groups: 2
## The size of the linkage groups are: 126 1
## The number of bins in each linkage group: 65 1
## caution! marker: S1D_108800874 was killed due to too many missing genotype calls
## caution! marker: S1D_12336853 was killed due to too many missing genotype calls
## caution! marker: S1D_126207663 was killed due to too many missing genotype calls
## caution! marker: S1D_18994718 was killed due to too many missing genotype calls
## caution! marker: S1D_195188775 was killed due to too many missing genotype calls
## caution! marker: S1D_20898473 was killed due to too many missing genotype calls
## caution! marker: S1D_30511606 was killed due to too many missing genotype calls
## caution! marker: S1D_314114216 was killed due to too many missing genotype calls
## caution! marker: S1D_31421492 was killed due to too many missing genotype calls
## caution! marker: S1D_415213577 was killed due to too many missing genotype calls
## caution! marker: S1D_415998037 was killed due to too many missing genotype calls
## caution! marker: S1D_416307975 was killed due to too many missing genotype calls
## caution! marker: S1D_418296747 was killed due to too many missing genotype calls
## caution! marker: S1D_42037618 was killed due to too many missing genotype calls
## caution! marker: S1D_420647502 was killed due to too many missing genotype calls
## caution! marker: S1D_420713054 was killed due to too many missing genotype calls
## caution! marker: S1D_434275732 was killed due to too many missing genotype calls
## caution! marker: S1D_434283648 was killed due to too many missing genotype calls
## caution! marker: S1D_436084543 was killed due to too many missing genotype calls
## caution! marker: S1D_45693844 was killed due to too many missing genotype calls
## caution! marker: S1D_55414084 was killed due to too many missing genotype calls
## caution! marker: S1D_8128584 was killed due to too many missing genotype calls
## caution! marker: S1D_99546052 was killed due to too many missing genotype calls
## Number of linkage groups: 7
## The size of the linkage groups are: 47 1 1 1 3 1 1
## The number of bins in each linkage group: 28 1 1 1 1 1 1
## caution! marker: S2A_13813846 was killed due to too many missing genotype calls
## caution! marker: S2A_14934024 was killed due to too many missing genotype calls
## caution! marker: S2A_31647170 was killed due to too many missing genotype calls
## caution! marker: S2A_3600808 was killed due to too many missing genotype calls
## caution! marker: S2A_38307973 was killed due to too many missing genotype calls
## caution! marker: S2A_44183357 was killed due to too many missing genotype calls
## caution! marker: S2A_45189484 was killed due to too many missing genotype calls
## caution! marker: S2A_48253236 was killed due to too many missing genotype calls
## caution! marker: S2A_58353805 was killed due to too many missing genotype calls
## caution! marker: S2A_58361862 was killed due to too many missing genotype calls
## caution! marker: S2A_59565903 was killed due to too many missing genotype calls
## caution! marker: S2A_60773513 was killed due to too many missing genotype calls
## caution! marker: S2A_692937811 was killed due to too many missing genotype calls
## caution! marker: S2A_696221512 was killed due to too many missing genotype calls
## caution! marker: S2A_696243418 was killed due to too many missing genotype calls
## caution! marker: S2A_700349740 was killed due to too many missing genotype calls
## caution! marker: S2A_703111105 was killed due to too many missing genotype calls
## caution! marker: S2A_703391992 was killed due to too many missing genotype calls
## caution! marker: S2A_705524350 was killed due to too many missing genotype calls
## caution! marker: S2A_705790851 was killed due to too many missing genotype calls
## caution! marker: S2A_706756678 was killed due to too many missing genotype calls
## caution! marker: S2A_708156051 was killed due to too many missing genotype calls
## caution! marker: S2A_711956900 was killed due to too many missing genotype calls
## caution! marker: S2A_712313909 was killed due to too many missing genotype calls
## caution! marker: S2A_712846295 was killed due to too many missing genotype calls
## caution! marker: S2A_713127471 was killed due to too many missing genotype calls
## caution! marker: S2A_716544664 was killed due to too many missing genotype calls
## caution! marker: S2A_716906727 was killed due to too many missing genotype calls
## caution! marker: S2A_717819140 was killed due to too many missing genotype calls
## caution! marker: S2A_717940571 was killed due to too many missing genotype calls
## caution! marker: S2A_726224355 was killed due to too many missing genotype calls
## caution! marker: S2A_726296233 was killed due to too many missing genotype calls
## caution! marker: S2A_727245830 was killed due to too many missing genotype calls
## caution! marker: S2A_727951622 was killed due to too many missing genotype calls
## caution! marker: S2A_734275042 was killed due to too many missing genotype calls
## caution! marker: S2A_735244212 was killed due to too many missing genotype calls
## caution! marker: S2A_735701887 was killed due to too many missing genotype calls
## caution! marker: S2A_741832734 was killed due to too many missing genotype calls
## caution! marker: S2A_744725037 was killed due to too many missing genotype calls
## caution! marker: S2A_746797078 was killed due to too many missing genotype calls
## caution! marker: S2A_746845489 was killed due to too many missing genotype calls
## caution! marker: S2A_746935263 was killed due to too many missing genotype calls
## caution! marker: S2A_747676101 was killed due to too many missing genotype calls
## caution! marker: S2A_747676114 was killed due to too many missing genotype calls
## caution! marker: S2A_748503325 was killed due to too many missing genotype calls
## caution! marker: S2A_749902351 was killed due to too many missing genotype calls
## caution! marker: S2A_750550946 was killed due to too many missing genotype calls
## caution! marker: S2A_751661937 was killed due to too many missing genotype calls
## caution! marker: S2A_753268026 was killed due to too many missing genotype calls
## caution! marker: S2A_755740567 was killed due to too many missing genotype calls
## caution! marker: S2A_756804097 was killed due to too many missing genotype calls
## caution! marker: S2A_756928829 was killed due to too many missing genotype calls
## caution! marker: S2A_757940294 was killed due to too many missing genotype calls
## caution! marker: S2A_766701909 was killed due to too many missing genotype calls
## caution! marker: S2A_770713932 was killed due to too many missing genotype calls
## caution! marker: S2A_770977178 was killed due to too many missing genotype calls
## caution! marker: S2A_771714906 was killed due to too many missing genotype calls
## caution! marker: S2A_775552468 was killed due to too many missing genotype calls
## caution! marker: S2A_776552295 was killed due to too many missing genotype calls
## caution! marker: S2A_779809062 was killed due to too many missing genotype calls
## Number of linkage groups: 5
## The size of the linkage groups are: 116 1 1 1 1
## The number of bins in each linkage group: 63 1 1 1 1
## caution! marker: S2B_103905079 was killed due to too many missing genotype calls
## caution! marker: S2B_133690086 was killed due to too many missing genotype calls
## caution! marker: S2B_134703911 was killed due to too many missing genotype calls
## caution! marker: S2B_135693135 was killed due to too many missing genotype calls
## caution! marker: S2B_2114958 was killed due to too many missing genotype calls
## caution! marker: S2B_2364389 was killed due to too many missing genotype calls
## caution! marker: S2B_24136080 was killed due to too many missing genotype calls
## caution! marker: S2B_24907146 was killed due to too many missing genotype calls
## caution! marker: S2B_30465929 was killed due to too many missing genotype calls
## caution! marker: S2B_337083120 was killed due to too many missing genotype calls
## caution! marker: S2B_453631494 was killed due to too many missing genotype calls
## caution! marker: S2B_53429994 was killed due to too many missing genotype calls
## caution! marker: S2B_5666663 was killed due to too many missing genotype calls
## caution! marker: S2B_5782077 was killed due to too many missing genotype calls
## caution! marker: S2B_6264495 was killed due to too many missing genotype calls
## caution! marker: S2B_682939246 was killed due to too many missing genotype calls
## caution! marker: S2B_699695695 was killed due to too many missing genotype calls
## caution! marker: S2B_700740206 was killed due to too many missing genotype calls
## caution! marker: S2B_70804943 was killed due to too many missing genotype calls
## caution! marker: S2B_712959062 was killed due to too many missing genotype calls
## caution! marker: S2B_713939527 was killed due to too many missing genotype calls
## caution! marker: S2B_732224700 was killed due to too many missing genotype calls
## caution! marker: S2B_732342448 was killed due to too many missing genotype calls
## caution! marker: S2B_734266688 was killed due to too many missing genotype calls
## caution! marker: S2B_74837898 was killed due to too many missing genotype calls
## caution! marker: S2B_754667674 was killed due to too many missing genotype calls
## caution! marker: S2B_763506478 was killed due to too many missing genotype calls
## caution! marker: S2B_764073725 was killed due to too many missing genotype calls
## caution! marker: S2B_767121964 was killed due to too many missing genotype calls
## caution! marker: S2B_768025567 was killed due to too many missing genotype calls
## caution! marker: S2B_773465908 was killed due to too many missing genotype calls
## caution! marker: S2B_773807507 was killed due to too many missing genotype calls
## caution! marker: S2B_775396940 was killed due to too many missing genotype calls
## caution! marker: S2B_776263384 was killed due to too many missing genotype calls
## caution! marker: S2B_776409762 was killed due to too many missing genotype calls
## caution! marker: S2B_776529584 was killed due to too many missing genotype calls
## caution! marker: S2B_776529600 was killed due to too many missing genotype calls
## caution! marker: S2B_780595416 was killed due to too many missing genotype calls
## caution! marker: S2B_785074460 was killed due to too many missing genotype calls
## caution! marker: S2B_785188862 was killed due to too many missing genotype calls
## caution! marker: S2B_785188919 was killed due to too many missing genotype calls
## caution! marker: S2B_785879165 was killed due to too many missing genotype calls
## caution! marker: S2B_785905982 was killed due to too many missing genotype calls
## caution! marker: S2B_787820195 was killed due to too many missing genotype calls
## caution! marker: S2B_790234621 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 128 4 1 1
## The number of bins in each linkage group: 72 4 1 1
## caution! marker: S2D_13243107 was killed due to too many missing genotype calls
## caution! marker: S2D_15134145 was killed due to too many missing genotype calls
## caution! marker: S2D_57824943 was killed due to too many missing genotype calls
## caution! marker: S2D_58145168 was killed due to too many missing genotype calls
## caution! marker: S2D_63536732 was killed due to too many missing genotype calls
## caution! marker: S2D_648643877 was killed due to too many missing genotype calls
## Number of linkage groups: 9
## The size of the linkage groups are: 24 1 2 1 4 1 2 1 4
## The number of bins in each linkage group: 12 1 1 1 4 1 1 1 1
## caution! marker: S3A_10160485 was killed due to too many missing genotype calls
## caution! marker: S3A_12026119 was killed due to too many missing genotype calls
## caution! marker: S3A_12287287 was killed due to too many missing genotype calls
## caution! marker: S3A_12407944 was killed due to too many missing genotype calls
## caution! marker: S3A_1286032 was killed due to too many missing genotype calls
## caution! marker: S3A_1286136 was killed due to too many missing genotype calls
## caution! marker: S3A_12921895 was killed due to too many missing genotype calls
## caution! marker: S3A_13244308 was killed due to too many missing genotype calls
## caution! marker: S3A_144157542 was killed due to too many missing genotype calls
## caution! marker: S3A_15777532 was killed due to too many missing genotype calls
## caution! marker: S3A_1783449 was killed due to too many missing genotype calls
## caution! marker: S3A_19301092 was killed due to too many missing genotype calls
## caution! marker: S3A_20102450 was killed due to too many missing genotype calls
## caution! marker: S3A_20238739 was killed due to too many missing genotype calls
## caution! marker: S3A_22505423 was killed due to too many missing genotype calls
## caution! marker: S3A_36678555 was killed due to too many missing genotype calls
## caution! marker: S3A_37477876 was killed due to too many missing genotype calls
## caution! marker: S3A_45565013 was killed due to too many missing genotype calls
## caution! marker: S3A_478166110 was killed due to too many missing genotype calls
## caution! marker: S3A_483743430 was killed due to too many missing genotype calls
## caution! marker: S3A_510681430 was killed due to too many missing genotype calls
## caution! marker: S3A_512321079 was killed due to too many missing genotype calls
## caution! marker: S3A_512556966 was killed due to too many missing genotype calls
## caution! marker: S3A_52210597 was killed due to too many missing genotype calls
## caution! marker: S3A_550193790 was killed due to too many missing genotype calls
## caution! marker: S3A_550524499 was killed due to too many missing genotype calls
## caution! marker: S3A_552022102 was killed due to too many missing genotype calls
## caution! marker: S3A_556367956 was killed due to too many missing genotype calls
## caution! marker: S3A_565574099 was killed due to too many missing genotype calls
## caution! marker: S3A_56679828 was killed due to too many missing genotype calls
## caution! marker: S3A_570318062 was killed due to too many missing genotype calls
## caution! marker: S3A_570750031 was killed due to too many missing genotype calls
## caution! marker: S3A_572745436 was killed due to too many missing genotype calls
## caution! marker: S3A_600315918 was killed due to too many missing genotype calls
## caution! marker: S3A_61285901 was killed due to too many missing genotype calls
## caution! marker: S3A_628657586 was killed due to too many missing genotype calls
## caution! marker: S3A_68283065 was killed due to too many missing genotype calls
## caution! marker: S3A_684697646 was killed due to too many missing genotype calls
## caution! marker: S3A_688449924 was killed due to too many missing genotype calls
## caution! marker: S3A_688673469 was killed due to too many missing genotype calls
## caution! marker: S3A_691476010 was killed due to too many missing genotype calls
## caution! marker: S3A_695042689 was killed due to too many missing genotype calls
## caution! marker: S3A_697456503 was killed due to too many missing genotype calls
## caution! marker: S3A_699241752 was killed due to too many missing genotype calls
## caution! marker: S3A_701892218 was killed due to too many missing genotype calls
## caution! marker: S3A_702327328 was killed due to too many missing genotype calls
## caution! marker: S3A_705231937 was killed due to too many missing genotype calls
## caution! marker: S3A_707925210 was killed due to too many missing genotype calls
## caution! marker: S3A_709207023 was killed due to too many missing genotype calls
## caution! marker: S3A_714455882 was killed due to too many missing genotype calls
## caution! marker: S3A_716847059 was killed due to too many missing genotype calls
## caution! marker: S3A_722449145 was killed due to too many missing genotype calls
## caution! marker: S3A_724541183 was killed due to too many missing genotype calls
## caution! marker: S3A_726107417 was killed due to too many missing genotype calls
## caution! marker: S3A_729609875 was killed due to too many missing genotype calls
## caution! marker: S3A_731113222 was killed due to too many missing genotype calls
## caution! marker: S3A_731772531 was killed due to too many missing genotype calls
## caution! marker: S3A_73513370 was killed due to too many missing genotype calls
## caution! marker: S3A_737210825 was killed due to too many missing genotype calls
## caution! marker: S3A_88201254 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 165 1 1 1
## The number of bins in each linkage group: 61 1 1 1
## caution! marker: S3B_129530784 was killed due to too many missing genotype calls
## caution! marker: S3B_13505129 was killed due to too many missing genotype calls
## caution! marker: S3B_13680763 was killed due to too many missing genotype calls
## caution! marker: S3B_16589598 was killed due to too many missing genotype calls
## caution! marker: S3B_18484719 was killed due to too many missing genotype calls
## caution! marker: S3B_18569321 was killed due to too many missing genotype calls
## caution! marker: S3B_20070137 was killed due to too many missing genotype calls
## caution! marker: S3B_216180078 was killed due to too many missing genotype calls
## caution! marker: S3B_21770922 was killed due to too many missing genotype calls
## caution! marker: S3B_21863700 was killed due to too many missing genotype calls
## caution! marker: S3B_22129379 was killed due to too many missing genotype calls
## caution! marker: S3B_222247324 was killed due to too many missing genotype calls
## caution! marker: S3B_24940723 was killed due to too many missing genotype calls
## caution! marker: S3B_250270760 was killed due to too many missing genotype calls
## caution! marker: S3B_255038905 was killed due to too many missing genotype calls
## caution! marker: S3B_257999830 was killed due to too many missing genotype calls
## caution! marker: S3B_257999933 was killed due to too many missing genotype calls
## caution! marker: S3B_31366808 was killed due to too many missing genotype calls
## caution! marker: S3B_331898364 was killed due to too many missing genotype calls
## caution! marker: S3B_403907351 was killed due to too many missing genotype calls
## caution! marker: S3B_414828393 was killed due to too many missing genotype calls
## caution! marker: S3B_415897806 was killed due to too many missing genotype calls
## caution! marker: S3B_418826377 was killed due to too many missing genotype calls
## caution! marker: S3B_418882248 was killed due to too many missing genotype calls
## caution! marker: S3B_44274212 was killed due to too many missing genotype calls
## caution! marker: S3B_457011297 was killed due to too many missing genotype calls
## caution! marker: S3B_508711126 was killed due to too many missing genotype calls
## caution! marker: S3B_512743315 was killed due to too many missing genotype calls
## caution! marker: S3B_558500556 was killed due to too many missing genotype calls
## caution! marker: S3B_571534403 was killed due to too many missing genotype calls
## caution! marker: S3B_572679630 was killed due to too many missing genotype calls
## caution! marker: S3B_580784925 was killed due to too many missing genotype calls
## caution! marker: S3B_581263452 was killed due to too many missing genotype calls
## caution! marker: S3B_581976209 was killed due to too many missing genotype calls
## caution! marker: S3B_59889587 was killed due to too many missing genotype calls
## caution! marker: S3B_598995503 was killed due to too many missing genotype calls
## caution! marker: S3B_602786401 was killed due to too many missing genotype calls
## caution! marker: S3B_603576794 was killed due to too many missing genotype calls
## caution! marker: S3B_621472372 was killed due to too many missing genotype calls
## caution! marker: S3B_625779659 was killed due to too many missing genotype calls
## caution! marker: S3B_641985045 was killed due to too many missing genotype calls
## caution! marker: S3B_651703738 was killed due to too many missing genotype calls
## caution! marker: S3B_654656807 was killed due to too many missing genotype calls
## caution! marker: S3B_665244915 was killed due to too many missing genotype calls
## caution! marker: S3B_670252382 was killed due to too many missing genotype calls
## caution! marker: S3B_672873770 was killed due to too many missing genotype calls
## caution! marker: S3B_683106967 was killed due to too many missing genotype calls
## caution! marker: S3B_686735291 was killed due to too many missing genotype calls
## caution! marker: S3B_693086678 was killed due to too many missing genotype calls
## caution! marker: S3B_719818208 was killed due to too many missing genotype calls
## caution! marker: S3B_734436563 was killed due to too many missing genotype calls
## caution! marker: S3B_752439400 was killed due to too many missing genotype calls
## caution! marker: S3B_758201335 was killed due to too many missing genotype calls
## caution! marker: S3B_758373800 was killed due to too many missing genotype calls
## caution! marker: S3B_758373819 was killed due to too many missing genotype calls
## caution! marker: S3B_762027806 was killed due to too many missing genotype calls
## caution! marker: S3B_763801275 was killed due to too many missing genotype calls
## caution! marker: S3B_764076592 was killed due to too many missing genotype calls
## caution! marker: S3B_764334304 was killed due to too many missing genotype calls
## caution! marker: S3B_769190306 was killed due to too many missing genotype calls
## caution! marker: S3B_771390243 was killed due to too many missing genotype calls
## caution! marker: S3B_771761917 was killed due to too many missing genotype calls
## caution! marker: S3B_773850693 was killed due to too many missing genotype calls
## caution! marker: S3B_798395496 was killed due to too many missing genotype calls
## caution! marker: S3B_800944831 was killed due to too many missing genotype calls
## caution! marker: S3B_815300892 was killed due to too many missing genotype calls
## caution! marker: S3B_818513872 was killed due to too many missing genotype calls
## caution! marker: S3B_823078014 was killed due to too many missing genotype calls
## caution! marker: S3B_828672970 was killed due to too many missing genotype calls
## caution! marker: S3B_828795956 was killed due to too many missing genotype calls
## caution! marker: S3B_829023452 was killed due to too many missing genotype calls
## caution! marker: S3B_9439570 was killed due to too many missing genotype calls
## caution! marker: S3B_97080266 was killed due to too many missing genotype calls
## caution! marker: S3B_9927054 was killed due to too many missing genotype calls
## Number of linkage groups: 5
## The size of the linkage groups are: 216 1 1 1 1
## The number of bins in each linkage group: 91 1 1 1 1
## caution! marker: S3D_423130593 was killed due to too many missing genotype calls
## caution! marker: S3D_43280177 was killed due to too many missing genotype calls
## caution! marker: S3D_501076107 was killed due to too many missing genotype calls
## caution! marker: S3D_532205636 was killed due to too many missing genotype calls
## caution! marker: S3D_573418141 was killed due to too many missing genotype calls
## caution! marker: S3D_598540484 was killed due to too many missing genotype calls
## caution! marker: S3D_598671019 was killed due to too many missing genotype calls
## caution! marker: S3D_604968955 was killed due to too many missing genotype calls
## Number of linkage groups: 7
## The size of the linkage groups are: 1 1 6 1 1 1 7
## The number of bins in each linkage group: 1 1 6 1 1 1 3
## caution! marker: S4A_12604826 was killed due to too many missing genotype calls
## caution! marker: S4A_17889491 was killed due to too many missing genotype calls
## caution! marker: S4A_231000971 was killed due to too many missing genotype calls
## caution! marker: S4A_240020977 was killed due to too many missing genotype calls
## caution! marker: S4A_245001282 was killed due to too many missing genotype calls
## caution! marker: S4A_274104875 was killed due to too many missing genotype calls
## caution! marker: S4A_29387619 was killed due to too many missing genotype calls
## caution! marker: S4A_29456167 was killed due to too many missing genotype calls
## caution! marker: S4A_379465303 was killed due to too many missing genotype calls
## caution! marker: S4A_45144499 was killed due to too many missing genotype calls
## caution! marker: S4A_570267431 was killed due to too many missing genotype calls
## caution! marker: S4A_583968823 was killed due to too many missing genotype calls
## caution! marker: S4A_595433072 was killed due to too many missing genotype calls
## caution! marker: S4A_599575654 was killed due to too many missing genotype calls
## caution! marker: S4A_599647431 was killed due to too many missing genotype calls
## caution! marker: S4A_603046898 was killed due to too many missing genotype calls
## caution! marker: S4A_620342297 was killed due to too many missing genotype calls
## caution! marker: S4A_620613603 was killed due to too many missing genotype calls
## caution! marker: S4A_636102981 was killed due to too many missing genotype calls
## caution! marker: S4A_649801931 was killed due to too many missing genotype calls
## caution! marker: S4A_659210035 was killed due to too many missing genotype calls
## caution! marker: S4A_659705441 was killed due to too many missing genotype calls
## caution! marker: S4A_660455609 was killed due to too many missing genotype calls
## caution! marker: S4A_677941179 was killed due to too many missing genotype calls
## caution! marker: S4A_696459585 was killed due to too many missing genotype calls
## caution! marker: S4A_702126220 was killed due to too many missing genotype calls
## caution! marker: S4A_704255713 was killed due to too many missing genotype calls
## caution! marker: S4A_706675813 was killed due to too many missing genotype calls
## caution! marker: S4A_708103624 was killed due to too many missing genotype calls
## caution! marker: S4A_709206204 was killed due to too many missing genotype calls
## caution! marker: S4A_717948750 was killed due to too many missing genotype calls
## caution! marker: S4A_719112618 was killed due to too many missing genotype calls
## caution! marker: S4A_719933953 was killed due to too many missing genotype calls
## caution! marker: S4A_728280684 was killed due to too many missing genotype calls
## caution! marker: S4A_729872889 was killed due to too many missing genotype calls
## caution! marker: S4A_729895763 was killed due to too many missing genotype calls
## caution! marker: S4A_739080749 was killed due to too many missing genotype calls
## Number of linkage groups: 2
## The size of the linkage groups are: 117 4
## The number of bins in each linkage group: 51 3
## caution! marker: S4B_132328665 was killed due to too many missing genotype calls
## caution! marker: S4B_169921939 was killed due to too many missing genotype calls
## caution! marker: S4B_18655478 was killed due to too many missing genotype calls
## caution! marker: S4B_25485349 was killed due to too many missing genotype calls
## caution! marker: S4B_44943599 was killed due to too many missing genotype calls
## caution! marker: S4B_4852517 was killed due to too many missing genotype calls
## caution! marker: S4B_517960897 was killed due to too many missing genotype calls
## caution! marker: S4B_520404738 was killed due to too many missing genotype calls
## caution! marker: S4B_535089159 was killed due to too many missing genotype calls
## caution! marker: S4B_540718899 was killed due to too many missing genotype calls
## caution! marker: S4B_546023632 was killed due to too many missing genotype calls
## caution! marker: S4B_548121101 was killed due to too many missing genotype calls
## caution! marker: S4B_548121313 was killed due to too many missing genotype calls
## caution! marker: S4B_558060734 was killed due to too many missing genotype calls
## caution! marker: S4B_561020982 was killed due to too many missing genotype calls
## caution! marker: S4B_562355380 was killed due to too many missing genotype calls
## caution! marker: S4B_576500004 was killed due to too many missing genotype calls
## caution! marker: S4B_580774848 was killed due to too many missing genotype calls
## caution! marker: S4B_585694536 was killed due to too many missing genotype calls
## caution! marker: S4B_587061757 was killed due to too many missing genotype calls
## caution! marker: S4B_589181090 was killed due to too many missing genotype calls
## caution! marker: S4B_600166469 was killed due to too many missing genotype calls
## caution! marker: S4B_619589061 was killed due to too many missing genotype calls
## caution! marker: S4B_619935881 was killed due to too many missing genotype calls
## caution! marker: S4B_631889174 was killed due to too many missing genotype calls
## caution! marker: S4B_636460537 was killed due to too many missing genotype calls
## caution! marker: S4B_646330848 was killed due to too many missing genotype calls
## caution! marker: S4B_649578002 was killed due to too many missing genotype calls
## caution! marker: S4B_652407432 was killed due to too many missing genotype calls
## caution! marker: S4B_652980104 was killed due to too many missing genotype calls
## caution! marker: S4B_657003274 was killed due to too many missing genotype calls
## caution! marker: S4B_657992972 was killed due to too many missing genotype calls
## caution! marker: S4B_660653849 was killed due to too many missing genotype calls
## caution! marker: S4B_660664876 was killed due to too many missing genotype calls
## caution! marker: S4B_663621978 was killed due to too many missing genotype calls
## caution! marker: S4B_663662540 was killed due to too many missing genotype calls
## caution! marker: S4B_664198613 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 63 1 1 1
## The number of bins in each linkage group: 28 1 1 1
## caution! marker: S4D_455285893 was killed due to too many missing genotype calls
## caution! marker: S4D_455763127 was killed due to too many missing genotype calls
## caution! marker: S4D_456054449 was killed due to too many missing genotype calls
## Number of linkage groups: 2
## The size of the linkage groups are: 1 13
## The number of bins in each linkage group: 1 9
## caution! marker: S5A_10301112 was killed due to too many missing genotype calls
## caution! marker: S5A_11281441 was killed due to too many missing genotype calls
## caution! marker: S5A_11558716 was killed due to too many missing genotype calls
## caution! marker: S5A_18923304 was killed due to too many missing genotype calls
## caution! marker: S5A_27741539 was killed due to too many missing genotype calls
## caution! marker: S5A_28394562 was killed due to too many missing genotype calls
## caution! marker: S5A_28601518 was killed due to too many missing genotype calls
## caution! marker: S5A_30060588 was killed due to too many missing genotype calls
## caution! marker: S5A_397506855 was killed due to too many missing genotype calls
## caution! marker: S5A_400574104 was killed due to too many missing genotype calls
## caution! marker: S5A_400776896 was killed due to too many missing genotype calls
## caution! marker: S5A_406537103 was killed due to too many missing genotype calls
## caution! marker: S5A_431894778 was killed due to too many missing genotype calls
## caution! marker: S5A_433203022 was killed due to too many missing genotype calls
## caution! marker: S5A_438355410 was killed due to too many missing genotype calls
## caution! marker: S5A_455128730 was killed due to too many missing genotype calls
## caution! marker: S5A_458562272 was killed due to too many missing genotype calls
## caution! marker: S5A_473644518 was killed due to too many missing genotype calls
## caution! marker: S5A_481923892 was killed due to too many missing genotype calls
## caution! marker: S5A_485470009 was killed due to too many missing genotype calls
## caution! marker: S5A_488047668 was killed due to too many missing genotype calls
## caution! marker: S5A_488051326 was killed due to too many missing genotype calls
## caution! marker: S5A_493600030 was killed due to too many missing genotype calls
## caution! marker: S5A_528309857 was killed due to too many missing genotype calls
## caution! marker: S5A_561660493 was killed due to too many missing genotype calls
## caution! marker: S5A_570193479 was killed due to too many missing genotype calls
## caution! marker: S5A_570791931 was killed due to too many missing genotype calls
## caution! marker: S5A_570937620 was killed due to too many missing genotype calls
## caution! marker: S5A_573040321 was killed due to too many missing genotype calls
## caution! marker: S5A_577795558 was killed due to too many missing genotype calls
## caution! marker: S5A_580935013 was killed due to too many missing genotype calls
## caution! marker: S5A_581488651 was killed due to too many missing genotype calls
## caution! marker: S5A_585223490 was killed due to too many missing genotype calls
## caution! marker: S5A_597279906 was killed due to too many missing genotype calls
## caution! marker: S5A_598072175 was killed due to too many missing genotype calls
## caution! marker: S5A_599376695 was killed due to too many missing genotype calls
## caution! marker: S5A_6179472 was killed due to too many missing genotype calls
## caution! marker: S5A_633455949 was killed due to too many missing genotype calls
## caution! marker: S5A_640103433 was killed due to too many missing genotype calls
## caution! marker: S5A_671385584 was killed due to too many missing genotype calls
## caution! marker: S5A_680073489 was killed due to too many missing genotype calls
## caution! marker: S5A_680903923 was killed due to too many missing genotype calls
## caution! marker: S5A_680980888 was killed due to too many missing genotype calls
## caution! marker: S5A_681251635 was killed due to too many missing genotype calls
## caution! marker: S5A_688359724 was killed due to too many missing genotype calls
## caution! marker: S5A_689416952 was killed due to too many missing genotype calls
## caution! marker: S5A_692272597 was killed due to too many missing genotype calls
## caution! marker: S5A_692832158 was killed due to too many missing genotype calls
## caution! marker: S5A_6982425 was killed due to too many missing genotype calls
## caution! marker: S5A_702504844 was killed due to too many missing genotype calls
## caution! marker: S5A_8238951 was killed due to too many missing genotype calls
## caution! marker: S5A_9845562 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 145 1 1 1
## The number of bins in each linkage group: 76 1 1 1
## caution! marker: S5B_2055531 was killed due to too many missing genotype calls
## caution! marker: S5B_231095747 was killed due to too many missing genotype calls
## caution! marker: S5B_448847093 was killed due to too many missing genotype calls
## caution! marker: S5B_458663156 was killed due to too many missing genotype calls
## caution! marker: S5B_471381890 was killed due to too many missing genotype calls
## caution! marker: S5B_473610078 was killed due to too many missing genotype calls
## caution! marker: S5B_489578862 was killed due to too many missing genotype calls
## caution! marker: S5B_545169845 was killed due to too many missing genotype calls
## caution! marker: S5B_547368152 was killed due to too many missing genotype calls
## caution! marker: S5B_548934823 was killed due to too many missing genotype calls
## caution! marker: S5B_549082730 was killed due to too many missing genotype calls
## caution! marker: S5B_549953533 was killed due to too many missing genotype calls
## caution! marker: S5B_550166737 was killed due to too many missing genotype calls
## caution! marker: S5B_550192735 was killed due to too many missing genotype calls
## caution! marker: S5B_585262568 was killed due to too many missing genotype calls
## caution! marker: S5B_588757384 was killed due to too many missing genotype calls
## caution! marker: S5B_595121907 was killed due to too many missing genotype calls
## caution! marker: S5B_596543883 was killed due to too many missing genotype calls
## caution! marker: S5B_596841873 was killed due to too many missing genotype calls
## caution! marker: S5B_60876109 was killed due to too many missing genotype calls
## caution! marker: S5B_615199008 was killed due to too many missing genotype calls
## caution! marker: S5B_621212430 was killed due to too many missing genotype calls
## caution! marker: S5B_622394626 was killed due to too many missing genotype calls
## caution! marker: S5B_640288558 was killed due to too many missing genotype calls
## caution! marker: S5B_640396431 was killed due to too many missing genotype calls
## caution! marker: S5B_665938696 was killed due to too many missing genotype calls
## caution! marker: S5B_679181735 was killed due to too many missing genotype calls
## caution! marker: S5B_67969399 was killed due to too many missing genotype calls
## caution! marker: S5B_680769401 was killed due to too many missing genotype calls
## caution! marker: S5B_70563357 was killed due to too many missing genotype calls
## caution! marker: S5B_710940763 was killed due to too many missing genotype calls
## caution! marker: S5B_710946636 was killed due to too many missing genotype calls
## caution! marker: S5B_71990789 was killed due to too many missing genotype calls
## caution! marker: S5B_81396103 was killed due to too many missing genotype calls
## Number of linkage groups: 2
## The size of the linkage groups are: 97 5
## The number of bins in each linkage group: 42 2
## caution! marker: S5D_27492889 was killed due to too many missing genotype calls
## caution! marker: S5D_439980160 was killed due to too many missing genotype calls
## caution! marker: S5D_440246810 was killed due to too many missing genotype calls
## caution! marker: S5D_549984729 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 2 15 1 4
## The number of bins in each linkage group: 2 11 1 3
## caution! marker: S6A_100363142 was killed due to too many missing genotype calls
## caution! marker: S6A_106479262 was killed due to too many missing genotype calls
## caution! marker: S6A_114455762 was killed due to too many missing genotype calls
## caution! marker: S6A_135562761 was killed due to too many missing genotype calls
## caution! marker: S6A_28410585 was killed due to too many missing genotype calls
## caution! marker: S6A_2953873 was killed due to too many missing genotype calls
## caution! marker: S6A_34545288 was killed due to too many missing genotype calls
## caution! marker: S6A_35836093 was killed due to too many missing genotype calls
## caution! marker: S6A_3691870 was killed due to too many missing genotype calls
## caution! marker: S6A_3691880 was killed due to too many missing genotype calls
## caution! marker: S6A_38379154 was killed due to too many missing genotype calls
## caution! marker: S6A_421525683 was killed due to too many missing genotype calls
## caution! marker: S6A_426173022 was killed due to too many missing genotype calls
## caution! marker: S6A_496830820 was killed due to too many missing genotype calls
## caution! marker: S6A_51769720 was killed due to too many missing genotype calls
## caution! marker: S6A_52797562 was killed due to too many missing genotype calls
## caution! marker: S6A_53158552 was killed due to too many missing genotype calls
## caution! marker: S6A_53159132 was killed due to too many missing genotype calls
## caution! marker: S6A_582511213 was killed due to too many missing genotype calls
## caution! marker: S6A_583094252 was killed due to too many missing genotype calls
## caution! marker: S6A_584719935 was killed due to too many missing genotype calls
## caution! marker: S6A_595165805 was killed due to too many missing genotype calls
## caution! marker: S6A_597782518 was killed due to too many missing genotype calls
## caution! marker: S6A_599172742 was killed due to too many missing genotype calls
## caution! marker: S6A_609479343 was killed due to too many missing genotype calls
## caution! marker: S6A_609550185 was killed due to too many missing genotype calls
## caution! marker: S6A_611197380 was killed due to too many missing genotype calls
## caution! marker: S6A_611414381 was killed due to too many missing genotype calls
## caution! marker: S6A_614663607 was killed due to too many missing genotype calls
## caution! marker: S6A_615815033 was killed due to too many missing genotype calls
## caution! marker: S6A_62812322 was killed due to too many missing genotype calls
## caution! marker: S6A_63210155 was killed due to too many missing genotype calls
## caution! marker: S6A_684328 was killed due to too many missing genotype calls
## caution! marker: S6A_7304018 was killed due to too many missing genotype calls
## caution! marker: S6A_74106770 was killed due to too many missing genotype calls
## caution! marker: S6A_74845696 was killed due to too many missing genotype calls
## caution! marker: S6A_76758286 was killed due to too many missing genotype calls
## caution! marker: S6A_90214473 was killed due to too many missing genotype calls
## caution! marker: S6A_91330099 was killed due to too many missing genotype calls
## caution! marker: S6A_91882491 was killed due to too many missing genotype calls
## caution! marker: S6A_93205668 was killed due to too many missing genotype calls
## caution! marker: S6A_956005 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 104 11 2 1
## The number of bins in each linkage group: 46 7 2 1
## caution! marker: S6B_101534607 was killed due to too many missing genotype calls
## caution! marker: S6B_101666627 was killed due to too many missing genotype calls
## caution! marker: S6B_107120686 was killed due to too many missing genotype calls
## caution! marker: S6B_107123257 was killed due to too many missing genotype calls
## caution! marker: S6B_107711988 was killed due to too many missing genotype calls
## caution! marker: S6B_120683262 was killed due to too many missing genotype calls
## caution! marker: S6B_123207887 was killed due to too many missing genotype calls
## caution! marker: S6B_124647441 was killed due to too many missing genotype calls
## caution! marker: S6B_130076243 was killed due to too many missing genotype calls
## caution! marker: S6B_153532546 was killed due to too many missing genotype calls
## caution! marker: S6B_158288115 was killed due to too many missing genotype calls
## caution! marker: S6B_15928467 was killed due to too many missing genotype calls
## caution! marker: S6B_163950110 was killed due to too many missing genotype calls
## caution! marker: S6B_200325559 was killed due to too many missing genotype calls
## caution! marker: S6B_27649803 was killed due to too many missing genotype calls
## caution! marker: S6B_28730009 was killed due to too many missing genotype calls
## caution! marker: S6B_34091815 was killed due to too many missing genotype calls
## caution! marker: S6B_35706383 was killed due to too many missing genotype calls
## caution! marker: S6B_42233236 was killed due to too many missing genotype calls
## caution! marker: S6B_457663546 was killed due to too many missing genotype calls
## caution! marker: S6B_467911691 was killed due to too many missing genotype calls
## caution! marker: S6B_474215642 was killed due to too many missing genotype calls
## caution! marker: S6B_4889493 was killed due to too many missing genotype calls
## caution! marker: S6B_675392193 was killed due to too many missing genotype calls
## caution! marker: S6B_676372396 was killed due to too many missing genotype calls
## caution! marker: S6B_679640228 was killed due to too many missing genotype calls
## caution! marker: S6B_679743920 was killed due to too many missing genotype calls
## caution! marker: S6B_681205257 was killed due to too many missing genotype calls
## caution! marker: S6B_681427605 was killed due to too many missing genotype calls
## caution! marker: S6B_696080382 was killed due to too many missing genotype calls
## caution! marker: S6B_699542793 was killed due to too many missing genotype calls
## caution! marker: S6B_710012334 was killed due to too many missing genotype calls
## caution! marker: S6B_712227046 was killed due to too many missing genotype calls
## caution! marker: S6B_713463843 was killed due to too many missing genotype calls
## caution! marker: S6B_714642535 was killed due to too many missing genotype calls
## caution! marker: S6B_715630909 was killed due to too many missing genotype calls
## caution! marker: S6B_715938382 was killed due to too many missing genotype calls
## caution! marker: S6B_717419685 was killed due to too many missing genotype calls
## caution! marker: S6B_98659486 was killed due to too many missing genotype calls
## Number of linkage groups: 3
## The size of the linkage groups are: 116 1 1
## The number of bins in each linkage group: 52 1 1
## caution! marker: S6D_10510892 was killed due to too many missing genotype calls
## caution! marker: S6D_20088111 was killed due to too many missing genotype calls
## caution! marker: S6D_20802028 was killed due to too many missing genotype calls
## caution! marker: S6D_2098364 was killed due to too many missing genotype calls
## caution! marker: S6D_336361538 was killed due to too many missing genotype calls
## caution! marker: S6D_341460765 was killed due to too many missing genotype calls
## caution! marker: S6D_4425040 was killed due to too many missing genotype calls
## caution! marker: S6D_454940719 was killed due to too many missing genotype calls
## caution! marker: S6D_455735681 was killed due to too many missing genotype calls
## caution! marker: S6D_455735940 was killed due to too many missing genotype calls
## caution! marker: S6D_456325441 was killed due to too many missing genotype calls
## caution! marker: S6D_456367377 was killed due to too many missing genotype calls
## caution! marker: S6D_457723702 was killed due to too many missing genotype calls
## caution! marker: S6D_460103586 was killed due to too many missing genotype calls
## caution! marker: S6D_460468227 was killed due to too many missing genotype calls
## caution! marker: S6D_4627111 was killed due to too many missing genotype calls
## caution! marker: S6D_4629773 was killed due to too many missing genotype calls
## caution! marker: S6D_463680438 was killed due to too many missing genotype calls
## caution! marker: S6D_466537410 was killed due to too many missing genotype calls
## caution! marker: S6D_5845567 was killed due to too many missing genotype calls
## caution! marker: S6D_6745772 was killed due to too many missing genotype calls
## caution! marker: S6D_919226 was killed due to too many missing genotype calls
## Number of linkage groups: 2
## The size of the linkage groups are: 48 2
## The number of bins in each linkage group: 23 2
## caution! marker: S7A_12913553 was killed due to too many missing genotype calls
## caution! marker: S7A_13179057 was killed due to too many missing genotype calls
## caution! marker: S7A_1525093 was killed due to too many missing genotype calls
## caution! marker: S7A_16302867 was killed due to too many missing genotype calls
## caution! marker: S7A_163372914 was killed due to too many missing genotype calls
## caution! marker: S7A_19178431 was killed due to too many missing genotype calls
## caution! marker: S7A_22154933 was killed due to too many missing genotype calls
## caution! marker: S7A_22894976 was killed due to too many missing genotype calls
## caution! marker: S7A_248334159 was killed due to too many missing genotype calls
## caution! marker: S7A_24940330 was killed due to too many missing genotype calls
## caution! marker: S7A_261396954 was killed due to too many missing genotype calls
## caution! marker: S7A_26768261 was killed due to too many missing genotype calls
## caution! marker: S7A_279992545 was killed due to too many missing genotype calls
## caution! marker: S7A_288054873 was killed due to too many missing genotype calls
## caution! marker: S7A_29250060 was killed due to too many missing genotype calls
## caution! marker: S7A_29491762 was killed due to too many missing genotype calls
## caution! marker: S7A_30089307 was killed due to too many missing genotype calls
## caution! marker: S7A_31133774 was killed due to too many missing genotype calls
## caution! marker: S7A_32179154 was killed due to too many missing genotype calls
## caution! marker: S7A_32952522 was killed due to too many missing genotype calls
## caution! marker: S7A_335910317 was killed due to too many missing genotype calls
## caution! marker: S7A_3426596 was killed due to too many missing genotype calls
## caution! marker: S7A_35769647 was killed due to too many missing genotype calls
## caution! marker: S7A_36633756 was killed due to too many missing genotype calls
## caution! marker: S7A_41924480 was killed due to too many missing genotype calls
## caution! marker: S7A_42498101 was killed due to too many missing genotype calls
## caution! marker: S7A_4463112 was killed due to too many missing genotype calls
## caution! marker: S7A_47049602 was killed due to too many missing genotype calls
## caution! marker: S7A_47709128 was killed due to too many missing genotype calls
## caution! marker: S7A_514434843 was killed due to too many missing genotype calls
## caution! marker: S7A_514898952 was killed due to too many missing genotype calls
## caution! marker: S7A_53892882 was killed due to too many missing genotype calls
## caution! marker: S7A_545935120 was killed due to too many missing genotype calls
## caution! marker: S7A_562613157 was killed due to too many missing genotype calls
## caution! marker: S7A_568117361 was killed due to too many missing genotype calls
## caution! marker: S7A_569088259 was killed due to too many missing genotype calls
## caution! marker: S7A_573097562 was killed due to too many missing genotype calls
## caution! marker: S7A_573128811 was killed due to too many missing genotype calls
## caution! marker: S7A_639837597 was killed due to too many missing genotype calls
## caution! marker: S7A_641050335 was killed due to too many missing genotype calls
## caution! marker: S7A_646930742 was killed due to too many missing genotype calls
## caution! marker: S7A_648397710 was killed due to too many missing genotype calls
## caution! marker: S7A_652356917 was killed due to too many missing genotype calls
## caution! marker: S7A_652760637 was killed due to too many missing genotype calls
## caution! marker: S7A_654563991 was killed due to too many missing genotype calls
## caution! marker: S7A_657418735 was killed due to too many missing genotype calls
## caution! marker: S7A_658913762 was killed due to too many missing genotype calls
## caution! marker: S7A_659407499 was killed due to too many missing genotype calls
## caution! marker: S7A_660936870 was killed due to too many missing genotype calls
## caution! marker: S7A_660936881 was killed due to too many missing genotype calls
## caution! marker: S7A_671934015 was killed due to too many missing genotype calls
## caution! marker: S7A_675198718 was killed due to too many missing genotype calls
## caution! marker: S7A_676422862 was killed due to too many missing genotype calls
## caution! marker: S7A_677723013 was killed due to too many missing genotype calls
## caution! marker: S7A_681696004 was killed due to too many missing genotype calls
## caution! marker: S7A_681708060 was killed due to too many missing genotype calls
## caution! marker: S7A_68188764 was killed due to too many missing genotype calls
## caution! marker: S7A_682078313 was killed due to too many missing genotype calls
## caution! marker: S7A_682197682 was killed due to too many missing genotype calls
## caution! marker: S7A_683515686 was killed due to too many missing genotype calls
## caution! marker: S7A_684264902 was killed due to too many missing genotype calls
## caution! marker: S7A_688877776 was killed due to too many missing genotype calls
## caution! marker: S7A_690839620 was killed due to too many missing genotype calls
## caution! marker: S7A_692202279 was killed due to too many missing genotype calls
## caution! marker: S7A_694885254 was killed due to too many missing genotype calls
## caution! marker: S7A_697067660 was killed due to too many missing genotype calls
## caution! marker: S7A_699149790 was killed due to too many missing genotype calls
## caution! marker: S7A_700067019 was killed due to too many missing genotype calls
## caution! marker: S7A_705593635 was killed due to too many missing genotype calls
## caution! marker: S7A_705593710 was killed due to too many missing genotype calls
## caution! marker: S7A_716611061 was killed due to too many missing genotype calls
## caution! marker: S7A_720348490 was killed due to too many missing genotype calls
## caution! marker: S7A_730508998 was killed due to too many missing genotype calls
## caution! marker: S7A_731131068 was killed due to too many missing genotype calls
## caution! marker: S7A_7592185 was killed due to too many missing genotype calls
## caution! marker: S7A_78464287 was killed due to too many missing genotype calls
## caution! marker: S7A_82501050 was killed due to too many missing genotype calls
## caution! marker: S7A_8263550 was killed due to too many missing genotype calls
## caution! marker: S7A_82946533 was killed due to too many missing genotype calls
## caution! marker: S7A_82950729 was killed due to too many missing genotype calls
## caution! marker: S7A_8774152 was killed due to too many missing genotype calls
## Number of linkage groups: 5
## The size of the linkage groups are: 207 1 1 1 1
## The number of bins in each linkage group: 110 1 1 1 1
## caution! marker: S7B_10160986 was killed due to too many missing genotype calls
## caution! marker: S7B_124548883 was killed due to too many missing genotype calls
## caution! marker: S7B_125768996 was killed due to too many missing genotype calls
## caution! marker: S7B_13302267 was killed due to too many missing genotype calls
## caution! marker: S7B_24059487 was killed due to too many missing genotype calls
## caution! marker: S7B_24277247 was killed due to too many missing genotype calls
## caution! marker: S7B_24403262 was killed due to too many missing genotype calls
## caution! marker: S7B_3505031 was killed due to too many missing genotype calls
## caution! marker: S7B_36484350 was killed due to too many missing genotype calls
## caution! marker: S7B_37599825 was killed due to too many missing genotype calls
## caution! marker: S7B_41332104 was killed due to too many missing genotype calls
## caution! marker: S7B_41637766 was killed due to too many missing genotype calls
## caution! marker: S7B_44871836 was killed due to too many missing genotype calls
## caution! marker: S7B_468000773 was killed due to too many missing genotype calls
## caution! marker: S7B_488370374 was killed due to too many missing genotype calls
## caution! marker: S7B_60863539 was killed due to too many missing genotype calls
## caution! marker: S7B_62501249 was killed due to too many missing genotype calls
## caution! marker: S7B_64780941 was killed due to too many missing genotype calls
## caution! marker: S7B_651944265 was killed due to too many missing genotype calls
## caution! marker: S7B_651944323 was killed due to too many missing genotype calls
## caution! marker: S7B_653104564 was killed due to too many missing genotype calls
## caution! marker: S7B_65376968 was killed due to too many missing genotype calls
## caution! marker: S7B_653885689 was killed due to too many missing genotype calls
## caution! marker: S7B_655653769 was killed due to too many missing genotype calls
## caution! marker: S7B_680547034 was killed due to too many missing genotype calls
## caution! marker: S7B_68656051 was killed due to too many missing genotype calls
## caution! marker: S7B_698925394 was killed due to too many missing genotype calls
## caution! marker: S7B_701167748 was killed due to too many missing genotype calls
## caution! marker: S7B_703983462 was killed due to too many missing genotype calls
## caution! marker: S7B_707713993 was killed due to too many missing genotype calls
## caution! marker: S7B_709245392 was killed due to too many missing genotype calls
## caution! marker: S7B_709531013 was killed due to too many missing genotype calls
## caution! marker: S7B_709592360 was killed due to too many missing genotype calls
## caution! marker: S7B_712860131 was killed due to too many missing genotype calls
## caution! marker: S7B_713042159 was killed due to too many missing genotype calls
## caution! marker: S7B_716399493 was killed due to too many missing genotype calls
## caution! marker: S7B_719078053 was killed due to too many missing genotype calls
## caution! marker: S7B_720548366 was killed due to too many missing genotype calls
## caution! marker: S7B_720828759 was killed due to too many missing genotype calls
## caution! marker: S7B_724774915 was killed due to too many missing genotype calls
## caution! marker: S7B_725761332 was killed due to too many missing genotype calls
## caution! marker: S7B_726453091 was killed due to too many missing genotype calls
## caution! marker: S7B_727867443 was killed due to too many missing genotype calls
## caution! marker: S7B_733461150 was killed due to too many missing genotype calls
## caution! marker: S7B_736026429 was killed due to too many missing genotype calls
## caution! marker: S7B_742116525 was killed due to too many missing genotype calls
## caution! marker: S7B_745586402 was killed due to too many missing genotype calls
## caution! marker: S7B_801473 was killed due to too many missing genotype calls
## caution! marker: S7B_891235 was killed due to too many missing genotype calls
## Number of linkage groups: 4
## The size of the linkage groups are: 106 1 1 1
## The number of bins in each linkage group: 50 1 1 1
## caution! marker: S7D_10009686 was killed due to too many missing genotype calls
## caution! marker: S7D_10726864 was killed due to too many missing genotype calls
## caution! marker: S7D_109518724 was killed due to too many missing genotype calls
## caution! marker: S7D_109526849 was killed due to too many missing genotype calls
## caution! marker: S7D_112425066 was killed due to too many missing genotype calls
## caution! marker: S7D_11950645 was killed due to too many missing genotype calls
## caution! marker: S7D_14769127 was killed due to too many missing genotype calls
## caution! marker: S7D_155691310 was killed due to too many missing genotype calls
## caution! marker: S7D_201977415 was killed due to too many missing genotype calls
## caution! marker: S7D_220991869 was killed due to too many missing genotype calls
## caution! marker: S7D_399422889 was killed due to too many missing genotype calls
## caution! marker: S7D_412609384 was killed due to too many missing genotype calls
## caution! marker: S7D_514750308 was killed due to too many missing genotype calls
## caution! marker: S7D_515824464 was killed due to too many missing genotype calls
## caution! marker: S7D_529044228 was killed due to too many missing genotype calls
## caution! marker: S7D_5310891 was killed due to too many missing genotype calls
## caution! marker: S7D_55083057 was killed due to too many missing genotype calls
## caution! marker: S7D_58493344 was killed due to too many missing genotype calls
## caution! marker: S7D_58500973 was killed due to too many missing genotype calls
## caution! marker: S7D_6136243 was killed due to too many missing genotype calls
## caution! marker: S7D_6478038 was killed due to too many missing genotype calls
## caution! marker: S7D_82443694 was killed due to too many missing genotype calls
## caution! marker: S7D_8347181 was killed due to too many missing genotype calls
## Number of linkage groups: 8
## The size of the linkage groups are: 35 1 3 1 1 2 4 1
## The number of bins in each linkage group: 22 1 2 1 1 2 4 1
# Jitter map to improve marker distances
cross_file <- jittermap(cross_file)
# Subset and filter linkage groups
marker_count <- sapply(cross_file$geno, function(x) length(x$map))
marker_count <- data.frame(Chr = names(marker_count), n = marker_count) %>%
filter(n > 6 & !Chr %in% c("1A.2", "6A.2"))
cross_file <- subset(cross_file, chr = marker_count$Chr)
cross_file <- jittermap(cross_file)
# Rename linkage groups
names(cross_file$geno) <- chromosomes
# Remove unnecessary objects
remove(marker_count)
# Check map metrics
nphe(cross_file)
## [1] 29
nind(cross_file)
## [1] 166
nchr(cross_file)
## [1] 21
summary(cross_file)
## Doubled haploids
##
## No. individuals: 166
##
## No. phenotypes: 29
## Percent phenotyped: 100 99.4 99.4 99.4 100 98.2 98.2 98.2 98.2 97.6 98.2
## 98.2 98.2 98.2 98.8 98.8 98.8 98.8 98.2 98.2 98.2 94.6
## 97.6 98.8 98.8 98.8 98.8 98.8 98.8
##
## No. chromosomes: 21
## Autosomes: 1A 1B 1D 2A 2B 2D 3A 3B 3D 4A 4B 4D 5A 5B 5D 6A 6B 6D 7A
## 7B 7D
##
## Total markers: 1984
## No. markers: 89 126 47 116 128 24 165 216 7 117 63 13 145 97 15 104
## 116 48 207 106 35
## Percent genotyped: 92.4
## Genotypes (%): AA:49.5 BB:50.5
# Plot linkage map
plotMap(cross_file, horizontal = FALSE, shift = FALSE, main = "NC13-20076 Double Haploid Linkage Map")
# Function to compare cM vs BP position
compare_bp_to_cm <- function(x){
# Pull marker names
mn <- markernames(x)
# Pull positions of markers
pos <- find.markerpos(x, mn)
# Pull in rownames
pos <- rownames_to_column(pos, var = "BP_Location")
# Split the column BP_Location into a and b
pos <- tidyr::separate(pos, BP_Location, into = c("a", "b"), sep = "_")
# Rename columns
colnames(pos) <- c("trash", "BP", "Chr", "cM")
# Pull the three columns listed
pos <- pos[, c("Chr", "BP", "cM")]
# Bind marker names with their chromosme and positions
pos <- cbind(mn, pos)
# Rename columns
colnames(pos) <- c("Markers", "Chr", "BP", "cM")
# Make sure the BP and cM positions are numeric columns
pos[, 3:4] <- lapply(pos[, 3:4], as.numeric)
# Return the position dataframe
return(pos)
}
# Compare cM vs BP for each chromosome
pos <- compare_bp_to_cm(cross_file)
# Check and flip chromosome order if necessary
for (i in names(cross_file$geno)) {
# Make an object that has only the curren chromosome
q <- pos %>% filter(Chr == i)
# If the first makrer pysical positon is greater than the last marker pysical position
if (q[1, 3] > q[nrow(q), 3]) {
# Print message
print_message <- paste(paste("### Chromosome", i, "has been flipped ###"))
print(paste(rep("#", nchar(print_message)), collapse = ""))
print(print_message)
print(paste(rep("#", nchar(print_message)), collapse = ""))
# Then flip the linkage group
cross_file <- flip.order(cross_file, i)
# Remove the temporary dataframe
remove(q, print_message)
} else {
# Remove the temporary dataframe
remove(q)
}
}
## [1] "######################################"
## [1] "### Chromosome 3D has been flipped ###"
## [1] "######################################"
## [1] "######################################"
## [1] "### Chromosome 7D has been flipped ###"
## [1] "######################################"
# Re-calculate positions after potential flipping
pos <- compare_bp_to_cm(cross_file)
# Plot cM vs BP again
ggplot(data = pos, aes(x = cM, y = BP / 1000000)) +
geom_point() +
facet_wrap(~Chr, ncol = 3, scales = "fixed") +
coord_cartesian(ylim = c(0, 800), xlim = c(0, 250)) +
labs(title = "Centimorgan Position vs. Megabase Pair",
x = "Centimorgan (cM) Position",
y = "Megabase Pair (Mbp) Position")
# Save the plot
ggsave(plot = last_plot(),
filename = "cM_vs_bp_comparison.jpg",
units = "in", width = 8, height = 12, dpi = 320)
# Remove temporary objects
remove(pos)
# Write final cross file
write.cross(cross_file,
format = "csv",
filestem = "rqtl_input_file_final")
Find potential selfs
# # Find potential selfs
# potential_selfs <- pull.geno(cross_file)
# rownames(potential_selfs) <- cross_file$pheno$GENOTYPE
# potential_selfs <- t(potential_selfs)
#
# # make a function to get counts
# count_values <- function(column) {
# return(c(count_A = sum(column == 1, na.rm = TRUE),
# count_B = sum(column == 2, na.rm = TRUE),
# count_NA = sum(is.na(column))))
# }
#
# # Make an object to bind on
# check <- c()
#
# # Bind in
# for(i in 1:ncol(potential_selfs)){
#
# temp1<-data.frame(GENOTYPE=colnames(potential_selfs)[i],
# t(count_values(potential_selfs[,i])))
# temp1$x2<-c(chisq.test(x = temp1[,2:3], p = rep(0.5, 2))$statistic)
# temp1$p.value<-c(chisq.test(x = temp1[,2:3], p = rep(0.5, 2))$p.value)
# check <-rbind(check, temp1)
# remove(temp1)
#
# }
#
# # List of potential selfs
# potential_selfs <- check %>% filter(p.value<=1e-80)
#
# # Write selfs
# write.csv(potential_selfs,
# "list_of_potential_selfs.csv",
# row.names = FALSE)
QTL analysis without covariates - HD, PH, VR, FDK, DON
# Calculate marker probabilities
cross_file<-calc.genoprob(cross_file,
step=2.0,
off.end=0.0,
error.prob=1.0e-4,
map.function="kosambi",
stepwidth="fixed")
# Calculate marker probabilities for a simulated genotype
cross_file<-sim.geno(cross_file,
n.draws = 128,
step = 2,
off.end = 0.0,
error.prob=1.0e-4,
map.function="kosambi",
stepwidth="fixed")
# Set the number of permutations
nperms=1000
# Pull traits
traits<-names(cross_file$pheno)[-1]
# Covariates - set to null if none are specified
qtl_covariates <- NULL
# Make results vector
results<-list()
# Run initial interval mapping and pull out QTL
for (i in traits){
# Announce
print(paste("------------ Interval Mapping of", i,"------------"))
# Perform IM with multiple imputation method
print("Interval mapping...")
scans <- scanone(cross_file,
pheno.col = i,
addcovar = qtl_covariates,
model = "normal",
method = "hk")
print("Done")
# Perform IM permutations mapping to define significance threshold
print("Permutational interval mapping...")
perms <- scanone(cross_file,
pheno.col = i,
addcovar = qtl_covariates,
model = "normal",
method = "hk",
n.perm = nperms,
n.cluster = parallel::detectCores()-1) #set threshold
print("Done")
# Plot QTL Scan
print("Plotting...")
threshold <- summary(perms, alpha=0.05)
plot(scans,main=paste("IM for", i))
abline(h = threshold, lty = "dotted", lwd = 1, col="#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
# Print plot
jpeg(paste("Scan_IM_", i, ".jpg", sep = ""),
width = 11,
height = 4,
units = "in",
res = 320)
threshold <- summary(perms, alpha=0.05)
plot(scans, main = paste("IM for", i))
abline(h = threshold, lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
dev.off()
print("Done")
# Show the peak markers for QTL
print("Defining QTL...")
qtl <- summary(scans,
perm = perms,
lodcolum = 1,
alpha=0.05)
# Place outputs in lists
results$IM$scan[[i]] <- scans # Place the scan a the list
results$IM$perms[[i]] <- perms # Place the permutations a the list
results$IM$threshold[[i]] <- threshold # Place the thresholds a the list
# If not QTL is found
if(nrow(qtl)==0){
# Print out
print("No QTL identified in initial scan... Moving onto next trait!")
next
}
# Rename QTL to identify which scan they came from
qtl$name <- paste("IM_", qtl$chr, "-pos-", qtl$pos, sep = "")
print("Done")
# Define the QTL locations and effects
print("Drawing QTL...")
colnames(scans) <- c("chr", "pos", "lod")
# Set up objects for defining QTL
c <- qtl[,1] # Define the chromosomes where QTL are found
p <- qtl[,2] # Define the positions of the QTL
a <- subset(cross_file, chr=c) # Subset the chromosomes where QTL are found
# Make new cross with genome subset
a <- sim.geno(a,
n.draws = 128,
step = 2,
off.end = 0.0,
error.prob = 1.0e-4,
map.function = "kosambi",
stepwidth = "fixed")
# Make a QTL object from that subset
madeqtl <- makeqtl(a,
c,
p,
qtl.name = qtl[,4],
what = c("prob"))
# Place that QTL object in a list
results$IM$qtl[[i]] <- madeqtl
print("Done")
# Announce
print("Running drop-one QTL analysis to check significance...")
# Test the significance of those QTL using drop one analysis
qtlfit <- fitqtl(cross_file,
qtl = results$IM$qtl[[i]],
pheno.col = i,
model = "normal",
method = "hk")
# Remove insignificant QTL
if(!is.null(qtlfit$result.drop)){
# Make an object of the drop results of the fit QTL object
a <- as.data.frame(qtlfit$result.drop)
# Find the insignificant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Make a dataframe with insignificant QTL
a <- data.frame(name = rownames(a[a$sig==0,]))
# Drop those QTL
madeqtl <- dropfromqtl(madeqtl,
qtl.name = a$name)
# Put that in object
results$IM$qtl[[i]] <- madeqtl
# Remove temporary
remove(a,c,p,scans,perms,threshold)
}else{
# Remove temporary dataframes
remove(a,c,p,scans,perms,threshold)
}
# Set vectors outside the loop
j=1
# Make initial check object
qtl_check<-results$IM$qtl[[i]]
# Run MQM until no significant peaks
repeat{
# Announce
print(paste("-------- Performing Additional QTL Scan for Trait", i,"--------"))
# Run multiple QTL mapping
mqm <- addqtl(cross = cross_file,
pheno.col = i,
qtl = qtl_check,
covar = qtl_covariates,
method = "hk")
# Put that in the list
results[[paste("MQM", j, sep="")]]$scan[[i]] <- mqm
# Plot QTL Scan
print("Plotting...")
plot(mqm, main = paste("MQM", j, "for", i))
abline(h = results$IM$threshold[[i]], lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
print("Done")
# Write out picture to directory
jpeg(paste("Scan_",paste("MQM", j, sep=""),"_", i, ".jpg", sep = ""),
width = 11,
height = 4,
units = "in",
res = 320)
plot(mqm, main = paste("MQM", j, "for", i))
abline(h = results$IM$threshold[[i]], lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
dev.off()
#make a qtl object
qtl <- summary(mqm,
perm = results$IM$perms[[i]],
lodcolum = 1,
alpha = 0.05)
if(nrow(qtl)==0){
# Make a null object in the list of list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- NULL
# Announce
print(paste("There were no new QTL identified for", i, "aborting loop"))
# Break the loop
break
}else{
# Rename QTL to identify which scan they came from
qtl$name=paste(paste("MQM", j, "_", sep=""), qtl$chr,"-pos-",qtl$pos,sep="")
# Defining QTL
print("Drawing new QTL")
c<-qtl[,1] # Define the chromosomes where QTL are found
p<-qtl[,2] # Define the positions of the QTL
a<-subset(cross_file, chr=c) # Subset the chromosomes where QTL are found
# Simulate the genome for that subset
a <- sim.geno(a,
n.draws = 128,
step = 2,
off.end = 0.0,
error.prob = 1.0e-4,
map.function = "kosambi",
stepwidth = "fixed")
# Make a QTL object from that subset
madeqtl <- makeqtl(a,
c,
p,
qtl.name = qtl[,4],
what = c("prob"))
# Place that QTL object in a list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- madeqtl
print("Done")
# Announce
print("Running drop-one QTL analysis...")
# Test the significance of those QTL using dropone analysis
qtlfit <- fitqtl(cross_file,
qtl = madeqtl,
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Check results
if(!is.null(qtlfit$result.drop)){
# Announce
print("Checking drop-one analysis for significance")
# Make a dataframe of the drop results
a <- as.data.frame(qtlfit$result.drop)
# Check significance
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Check significance
a <- data.frame(name=rownames(a[a$sig==0,]))
# Separate the column name into sections
a <- tidyr::separate(data = a,
col = "name",
into = c("chr", "trash", "pos"),
sep = "-")
# Make sure position is a number
a$pos <- as.numeric(a$pos)
# Make the chromosome object from the trait
a$chr <- gsub(paste("MQM", j, "_", sep = ""),"",a$chr)
# Drop the insignificant QTL from the object
madeqtl <- dropfromqtl(madeqtl,
chr = a$chr,
pos = a$pos)
# Add the significant QTL to the object
madeqtl <- addtoqtl(cross_file,
qtl = madeqtl,
chr = qtl_check$chr,
pos = qtl_check$pos,
qtl.name = qtl_check$name)
# Add this QTL object to the list of list
results[[paste("MQM", j, sep="")]]$qtl[[i]] <- madeqtl
# Remove temporary dataframes
remove(a, c, p)
}else{
# Announce
print(paste("There was only one new QTL identified for ",
i,
" in ",
paste("MQM", j, sep = ""),
"... Checking significance!",
sep = ""))
# If this QTL is significant
if(qtlfit$result.full[1,6]<0.05){
# Announce
print("New QTL is significant, placing in total model!")
# Add that significant peak to the QTL object
madeqtl <- addtoqtl(cross_file,
qtl = madeqtl,
chr = qtl_check$chr,
pos = qtl_check$pos,
qtl.name = qtl_check$name)
# Add the object to the list of list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- madeqtl
}else{
# Make the multiple QTL model NULL
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- NULL
# Announce
print(paste("There were no new QTL identified for", i, "concluding loop."))
# Break loop
break
}
}
}
# Add result to object to check
qtl_check <- results[[paste("MQM", j, sep="")]]$qtl[[i]]
# Add 1 to the j vector to indicate what round we are on
j=j+1
}
# Check
if(j-1==0){
# Pull and make final qtl object
finalqtl <- results$IM$qtl[[i]]
}else{
# Pull and make final qtl object
finalqtl <- results[[paste("MQM", j-1, sep="")]]$qtl[[i]]
}
# Check for significance
qtlfit <- fitqtl(cross_file,
qtl = finalqtl,
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Rename the QTL
a <- finalqtl
a <- data.frame(summary(a))
a <- tidyr::separate(a, col = "name", into=c("model", "trash"), sep = "_")
a$name <- paste(a$model, "_", a$chr, "-pos-", a$pos, sep = "")
finalqtl$name = a$name
remove(a)
# Announce
print("Filtering insignificant markers...")
# If there are drop one analysis results
if(!is.null(qtlfit$result.drop)){
# Pull results into a dataframe
a <- as.data.frame(qtlfit$result.drop)
# Find the significant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Select the insignificant markers
a <- data.frame(name=rownames(a[a$sig==0,]))
# Remove those QTL from the final QTL object
finalqtl <- dropfromqtl(finalqtl,
qtl.name = a$name)
# Add this to the list of list
results$final$qtl_unrefine[[i]] <- finalqtl
# Remove the object
remove(a, finalqtl)
}else{
# Put the final object in the list of list
results$final$qtl_unrefine[[i]] <- finalqtl
# Remove the object finalqtl
remove(finalqtl)
}
# Announce
print("Refining QTL position...")
# Refine the QTL with the refine
results$final$qtl_refine[[i]] <- refineqtl(cross = cross_file,
pheno.col = i,
qtl = results$final$qtl_unrefine[[i]],
verbose = FALSE,
method = "hk")
# Rename the QTL
a <- results$final$qtl_refine[[i]]
a <- data.frame(summary(a))
a <- tidyr::separate(a, col = "name", into=c("model", "trash"), sep = "_")
a$name <- paste(a$model, "_", a$chr, "-pos-", a$pos, sep = "")
results$final$qtl_refine[[i]]$name <- a$name
remove(a)
# Final significance check
qtlfit <- fitqtl(cross_file,
qtl = results$final$qtl_refine[[i]],
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Put the final QTL object into finalqtl
finalqtl <- results$final$qtl_refine[[i]]
# If there are drop one analysis results
if(!is.null(qtlfit$result.drop)){
# Pull results into a dataframe
a <- as.data.frame(qtlfit$result.drop)
# Find the significant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Select the insignificant markers
a <- data.frame(name=rownames(a[a$sig==0,]))
# Remove those QTL from the final QTL object
finalqtl <- dropfromqtl(finalqtl,
qtl.name = a$name)
# Add this to the list of list
results$final$qtl_refine_filt[[i]] <- finalqtl
# Remove the object
remove(a, finalqtl)
}else{
# Put the final object in the list of list
results$final$qtl_refine_filt[[i]] <- finalqtl
# Remove the object finalqtl
remove(finalqtl)
}
# Create an object for support LOD internvals
droplodint<-c()
#create QTL 1.5 drop LOD intervals
for(z in results$final$qtl_refine_filt[[i]]$name){
# Pull data for specific QTL
a <- data.frame(name = z)
# Pull the scan name
a <- tidyr::separate(data = a,
col = "name",
into = c("scan", "other"),
sep = "_",
remove = FALSE)
# Pull the position name
a <- tidyr::separate(data = a,
col = "other",
into = c("chr", "trash", "pos"),
sep = "-",
remove = TRUE)
# Pull the relevant scan
b <- results[[a$scan]]$scan[[i]]
# Find the nearest sudo marker to the LOD peak
c <- find.pseudomarker(cross = cross_file,
chr = a$chr,
pos = as.numeric(a$pos))
# Pull the confidence interval for 1.5 LOD drop
d <- lodint(results = b,
chr = a$chr,
qtl.index = c,
drop = 1.5,
lodcolumn = 1,
expandtomarkers = TRUE)
# Create a dataframe with that data
d <- data.frame(name = z,
trait = i,
chr = unique(d$chr),
start_marker = rownames(d)[1],
start = d[1,2],
mid_marker = rownames(d)[2],
mid_pos = d[2,2],
stop_marker = rownames(d)[3],
stop = d[3,2])
# Bind into lodint object
droplodint <- rbind(droplodint, d)
# Remove temporary data
remove(a,b,c,d)
}
# Place in results
results$final$qtl_support_intervals[[i]] <- droplodint
# Remove
remove(droplodint)
}
## [1] "------------ Interval Mapping of VR_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for VR_ME in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait VR_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_ME in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 1 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of HD_ME ------------"
## [1] "Interval mapping..."
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## [1] "-------- Performing Additional QTL Scan for Trait HD_ME --------"
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## [1] "There was only one new QTL identified for HD_ME in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait HD_ME --------"
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for HD_ME aborting loop"
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## [1] "------------ Interval Mapping of PH_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait PH_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for PH_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_KIN19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of HD_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 4 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait HD_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 4 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for HD_KIN20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait HD_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 4 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for HD_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_KIN20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_KIN20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of PH_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait PH_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for PH_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of HD_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait HD_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for HD_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait HD_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for HD_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_RAL19 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of HD_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait HD_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for HD_RAL20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait HD_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for HD_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 3 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 9 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of PH_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 4 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait PH_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 4 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for PH_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_WAR19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_WAR19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_WAR19 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_WAR19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_WAR19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_WAR19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of HD_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait HD_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for HD_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 2 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
# Rename results
results_HD_PH_VR_FDK_DON<-results
# Remove results
remove(results)
Pull HD and PH Marker Covariates
# Pull QTL Support Intervals
covars <- rbind(results_HD_PH_VR_FDK_DON$final$qtl_support_intervals$HD_ME,
results_HD_PH_VR_FDK_DON$final$qtl_support_intervals$PH_ME)
# Make a dataframe
covar_markers <- data.frame(GENOTYPE = cross_file$pheno$GENOTYPE)
# For each QTL support intervals
for(i in 1:nrow(covars)){
# Take this row
a <- covars[i,]
# Then fill in the genome
b <- fill.geno(cross = cross_file,
method = c("no_dbl_XO"),
map.function = "kosambi")
# Pull the cross for the specific genome
b <- pull.geno(cross = b,
chr = a$chr)
# Pull the marker name
a <- find.marker(cross = cross_file,
chr = a$chr,
pos = a$mid_pos)
# Pull the data frame
b <- as.data.frame(b[,a])
# Rename the column
colnames(b) <- a
# Bind it into the dataframe
covar_markers <- cbind(covar_markers, b)
# Remove the temporary data
remove(a, b)
}
Visualize HD and PH QTL
# Pull vector of traits
traits <- names(cross_file$pheno)[c(grep("HD", names(cross_file$pheno)),
grep("PH", names(cross_file$pheno)))]
# Make a color pallet
color_palette <- c(
"HD_ME" = "#0000FF", # Blue
"HD_RAL19" = "#2F4F4F", # DarkSlateGray
"HD_RAL20" = "#00FFFF", # Cyan
"HD_KIN20" = "#1E90FF", # DodgerBlue
"HD_WAR20" = "#5F9EA0", # CadetBlue
"PH_ME" = "#EE82EE", # Violet
"PH_RAL20" = "#BA55D3", # MediumOrchid
"PH_KIN20" = "#9932CC" # DarkOrchid
)
# Make temp list out of results
dat_temp <- results_HD_PH_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_"))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j = j + 1
}
# Create final plot
a <- (finalplot$chr_4A+
finalplot$chr_5A+
finalplot$chr_6A+
finalplot$chr_6D+
finalplot$chr_7A+
finalplot$chr_7B)+
plot_layout(guides = "collect",
nrow = 1)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH) and Heading Date (HD)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_hd_and_ph.jpg",
width = 14,
height = 8,
dpi = 320,
units = "in")
Visualize HD and PH QTL for ME only
# Pull vector of traits
traits <- c("HD_ME", "PH_ME")
# Make a color pallet
color_palette <- c(
"Heading Date" = "#0000FF", # Blue
"Plant Height" = "#EE82EE" # Violet
)
# Make temp list out of results
dat_temp <- results_HD_PH_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_")) %>%
mutate(trait=ifelse(trait=="HD_ME", "Heading Date",
ifelse(trait=="PH_ME", "Plant Height", "ERROR")))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j=j+1
}
# Create final plot
a <- (finalplot$chr_4A+
finalplot$chr_5A+
finalplot$chr_6A+
finalplot$chr_6D+
finalplot$chr_7B)+
plot_layout(guides = "collect",
nrow = 1)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH) and Heading Date (HD)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_hd_and_ph_ME_only.jpg",
width = 14,
height = 8,
dpi = 320,
units = "in")
Visualize HD, PH, VR, FDK, and DON
# Pull vector of traits
traits <- names(cross_file$pheno)[-1]
# Make a color pallet
color_palette <- c(
"HD_ME" = "#0000FF", # Blue
"HD_RAL19" = "#2F4F4F", # DarkSlateGray
"HD_RAL20" = "#00FFFF", # Cyan
"HD_KIN20" = "#1E90FF", # DodgerBlue
"HD_WAR20" = "#5F9EA0", # CadetBlue
"PH_ME" = "#EE82EE", # Violet
"PH_RAL20" = "#BA55D3", # MediumOrchid
"PH_KIN20" = "#9932CC", # DarkOrchid
"VR_ME" = "#FF0000", # Red
"VR_KIN19" = "#8B0000", # DarkRed
"VR_KIN20" = "#B22280", # Firebrick
"VR_RAL19" = "#A0522D", # Brown
"VR_RAL20" = "#FF4500", # OrangeRed
"VR_WAR20" = "#FF6347", # Tomato
"FDK_ME" = "#FFFF00", # Yellow
"FDK_KIN19" = "#B8860B", # DarkGoldenrod
"FDK_KIN20" = "#FFD700", # Gold
"FDK_RAL19" = "#DAA520", # Goldenrod
"FDK_RAL20" = "#F0E68C", # Khaki
"FDK_WAR19" = "#EEE8AA", # PaleGoldenrod
"FDK_WAR20" = "#8B864E", # Khaki4
"DON_ME" = "#00FF00", # DarkGreen
"DON_KIN19" = "#66CD00", # Chartreuse3
"DON_KIN20" = "#556B2F", # DarkOliveGreen
"DON_RAL19" = "#8FBC8F", # DarkSeaGreen
"DON_RAL20" = "#6B8E23", # OliveDrab
"DON_WAR19" = "#98FB98", # PaleGreen
"DON_WAR20" = "#00FF7F" # SpringGreen1
)
# Make temp list out of results
dat_temp <- results_HD_PH_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_"))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j = j + 1
}
# Create final plot
a <- (finalplot$chr_1A+
finalplot$chr_2A+
finalplot$chr_2B+
finalplot$chr_3A+
finalplot$chr_3B+
finalplot$chr_4A+
finalplot$chr_5A+
finalplot$chr_5B+
finalplot$chr_6A+
finalplot$chr_6D+
finalplot$chr_7A+
finalplot$chr_7B+
finalplot$chr_7D)+
plot_layout(guides = "collect", nrow = 4)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH), Heading Date (HD), Visual Rating (VR), Fusarium Damaged Kernels (FDK), and Deoxynivalenol Content (DON)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_hd_ph_vr_fdk_don_unadjusted.jpg",
width = 20,
height = 15,
dpi = 320,
units = "in")
# Remove
remove(dat_ggplot, dat_temp)
Visualize HD, PH, VR, FDK, and DON for ME only
# Pull vector of traits
traits <- c("HD_ME", "PH_ME", "VR_ME", "FDK_ME", "DON_ME")
# Make a color pallet
color_palette <- c(
"Heading Date" = "#0000FF", # Blue
"Plant Height" = "#EE82EE", # Violet
"Visual Rating" = "#FF0000", # Red
"Fusarium Damaged Kernels" = "#FFFF00", # Yellow
"Deoxynivalenol Content" = "#00FF00" # Green
)
# Make temp list out of results
dat_temp <- results_HD_PH_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_")) %>%
mutate(trait=ifelse(trait=="HD_ME", "Heading Date",
ifelse(trait=="PH_ME", "Plant Height",
ifelse(trait=="VR_ME", "Visual Rating",
ifelse(trait=="FDK_ME", "Fusarium Damaged Kernels",
ifelse(trait=="DON_ME", "Deoxynivalenol Content", "ERROR"))))))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j = j + 1
}
# Create final plot
a <- (finalplot$chr_1A+
finalplot$chr_2A+
finalplot$chr_2B+
finalplot$chr_3A+
finalplot$chr_3B+
finalplot$chr_4A+
finalplot$chr_5A+
finalplot$chr_5B+
finalplot$chr_6A+
finalplot$chr_6D+
finalplot$chr_7A+
finalplot$chr_7B+
finalplot$chr_7D)+
plot_layout(guides = "collect", nrow = 4)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH), Heading Date (HD), Visual Rating (VR), Fusarium Damaged Kernels (FDK), and Deoxynivalenol Content (DON)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_hd_ph_vr_fdk_don_unadjusted_ME_only.jpg",
width = 20,
height = 15,
dpi = 320,
units = "in")
Percent variance calculations - HD, PH, VR, FDK, and DON without
adjustment
# Pull all traits
traits <- names(cross_file$pheno)[-1]
# Make an object for support internvals
final_qtls <- c()
# For i in trait names
for(i in traits){
# Pull temporary dataframe
a <- results_HD_PH_VR_FDK_DON$final$qtl_support_intervals[[i]]
# Format final QTL object
final_qtls <- rbind(final_qtls, a)
# Remove final object
remove(a)
}
# Make an object for information
qtl_info_drop_one <- c()
qtl_info_full <- c()
# For i in final_qtls object
for(i in traits){
# Pull data
a <- results_HD_PH_VR_FDK_DON$final$qtl_refine_filt[[i]]
# If a is NULL
if(is.null(a)){
# Move to next trait
next
}
# Fit QTL
b <- fitqtl(cross_file,
qtl =results_HD_PH_VR_FDK_DON$final$qtl_refine_filt[[i]],
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Get full model
c <- as.data.frame(b$result.full)
# Move effect into column name
c$effect <- rownames(c)
rownames(c) <- NULL
# Get drop one
d <- as.data.frame(b$result.drop)
# Move QTL name into column
d$name <- rownames(d)
rownames(d) <- NULL
# Bind in data
e <- results_HD_PH_VR_FDK_DON$final$qtl_support_intervals[[i]]
# Left join
d <- left_join(d, e, by = "name")
# Add trait names to both full model
c$trait <- i
# Bind in data
qtl_info_drop_one <- rbind(qtl_info_drop_one, d)
qtl_info_full <- rbind(qtl_info_full, c)
# Remove
remove(a,b,c,d,e)
}
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 4 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
# Write file
write.csv(qtl_info_drop_one,
"hd_ph_vr_fdk_don_drop_one_qtl_info_no_adjustment.csv",
row.names = FALSE)
# Write file
write.csv(qtl_info_full,
"hd_ph_vr_fdk_don_full_model_qtl_info_no_adjustment.csv",
row.names = FALSE)
QTL analysis with HD and PH covariates - VR, FDK, DON
# Set the number of permutations
nperms=1000
# Pull traits
traits<-names(cross_file$pheno)[c(grep("VR", names(cross_file$pheno)),
grep("FDK", names(cross_file$pheno)),
grep("DON", names(cross_file$pheno)))]
# Covariates - set to null if none are specified
qtl_covariates <- covar_markers[,2:ncol(covar_markers)]
# Make results vector
results<-list()
# Run initial interval mapping and pull out QTL
for (i in traits){
# Announce
print(paste("------------ Interval Mapping of", i,"------------"))
# Perform IM with multiple imputation method
print("Interval mapping...")
scans <- scanone(cross_file,
pheno.col = i,
addcovar = qtl_covariates,
model = "normal",
method = "hk")
print("Done")
# Perform IM permutations mapping to define significance threshold
print("Permutational interval mapping...")
perms <- scanone(cross_file,
pheno.col = i,
addcovar = qtl_covariates,
model = "normal",
method = "hk",
n.perm = nperms,
n.cluster = parallel::detectCores()-1) #set threshold
print("Done")
# Plot QTL Scan
print("Plotting...")
threshold <- summary(perms, alpha=0.05)
plot(scans,main=paste("IM for", i))
abline(h = threshold, lty = "dotted", lwd = 1, col="#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
# Print plot
jpeg(paste("Scan_IM_", i, "_With_Adjustment.jpg", sep = ""),
width = 11,
height = 4,
units = "in",
res = 320)
threshold <- summary(perms, alpha=0.05)
plot(scans, main = paste("IM for", i))
abline(h = threshold, lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
dev.off()
print("Done")
# Show the peak markers for QTL
print("Defining QTL...")
qtl <- summary(scans,
perm = perms,
lodcolum = 1,
alpha=0.05)
# Place outputs in lists
results$IM$scan[[i]] <- scans # Place the scan a the list
results$IM$perms[[i]] <- perms # Place the permutations a the list
results$IM$threshold[[i]] <- threshold # Place the thresholds a the list
# If not QTL is found
if(nrow(qtl)==0){
# Print out
print("No QTL identified in initial scan... Moving onto next trait!")
next
}
# Rename QTL to identify which scan they came from
qtl$name <- paste("IM_", qtl$chr, "-pos-", qtl$pos, sep = "")
print("Done")
# Define the QTL locations and effects
print("Drawing QTL...")
colnames(scans) <- c("chr", "pos", "lod")
# Set up objects for defining QTL
c <- qtl[,1] # Define the chromosomes where QTL are found
p <- qtl[,2] # Define the positions of the QTL
a <- subset(cross_file, chr=c) # Subset the chromosomes where QTL are found
# Make new cross with genome subset
a <- sim.geno(a,
n.draws = 128,
step = 2,
off.end = 0.0,
error.prob = 1.0e-4,
map.function = "kosambi",
stepwidth = "fixed")
# Make a QTL object from that subset
madeqtl <- makeqtl(a,
c,
p,
qtl.name = qtl[,4],
what = c("prob"))
# Place that QTL object in a list
results$IM$qtl[[i]] <- madeqtl
print("Done")
# Announce
print("Running drop-one QTL analysis to check significance...")
# Test the significance of those QTL using drop one analysis
qtlfit <- fitqtl(cross_file,
qtl = results$IM$qtl[[i]],
pheno.col = i,
model = "normal",
method = "hk")
# Remove insignificant QTL
if(!is.null(qtlfit$result.drop)){
# Make an object of the drop results of the fit QTL object
a <- as.data.frame(qtlfit$result.drop)
# Find the insignificant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Make a dataframe with insignificant QTL
a <- data.frame(name = rownames(a[a$sig==0,]))
# Drop those QTL
madeqtl <- dropfromqtl(madeqtl,
qtl.name = a$name)
# Put that in object
results$IM$qtl[[i]] <- madeqtl
# Remove temporary
remove(a,c,p,scans,perms,threshold)
}else{
# Remove temporary dataframes
remove(a,c,p,scans,perms,threshold)
}
# Set vectors outside the loop
j=1
# Make initial check object
qtl_check<-results$IM$qtl[[i]]
# Run MQM until no significant peaks
repeat{
# Announce
print(paste("-------- Performing Additional QTL Scan for Trait", i,"--------"))
# Run multiple QTL mapping
mqm <- addqtl(cross = cross_file,
pheno.col = i,
qtl = qtl_check,
covar = qtl_covariates,
method = "hk")
# Put that in the list
results[[paste("MQM", j, sep="")]]$scan[[i]] <- mqm
# Plot QTL Scan
print("Plotting...")
plot(mqm, main = paste("MQM", j, "for", i))
abline(h = results$IM$threshold[[i]], lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
print("Done")
# Write out picture to directory
jpeg(paste("Scan_",paste("MQM", j, sep=""),"_", i, "_With_Adjustment.jpg", sep = ""),
width = 11,
height = 4,
units = "in",
res = 320)
plot(mqm, main = paste("MQM", j, "for", i))
abline(h = results$IM$threshold[[i]], lty = "dotted", lwd = 1, col = "#cc0000")
legend("topleft",legend = c("p=0.05"),col = c("#cc0000"),lty = "dotted")
dev.off()
#make a qtl object
qtl <- summary(mqm,
perm = results$IM$perms[[i]],
lodcolum = 1,
alpha = 0.05)
if(nrow(qtl)==0){
# Make a null object in the list of list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- NULL
# Announce
print(paste("There were no new QTL identified for", i, "aborting loop"))
# Break the loop
break
}else{
# Rename QTL to identify which scan they came from
qtl$name=paste(paste("MQM", j, "_", sep=""), qtl$chr,"-pos-",qtl$pos,sep="")
# Defining QTL
print("Drawing new QTL")
c<-qtl[,1] # Define the chromosomes where QTL are found
p<-qtl[,2] # Define the positions of the QTL
a<-subset(cross_file, chr=c) # Subset the chromosomes where QTL are found
# Simulate the genome for that subset
a <- sim.geno(a,
n.draws = 128,
step = 2,
off.end = 0.0,
error.prob = 1.0e-4,
map.function = "kosambi",
stepwidth = "fixed")
# Make a QTL object from that subset
madeqtl <- makeqtl(a,
c,
p,
qtl.name = qtl[,4],
what = c("prob"))
# Place that QTL object in a list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- madeqtl
print("Done")
# Announce
print("Running drop-one QTL analysis...")
# Test the significance of those QTL using dropone analysis
qtlfit <- fitqtl(cross_file,
qtl = madeqtl,
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Check results
if(!is.null(qtlfit$result.drop)){
# Announce
print("Checking drop-one analysis for significance")
# Make a dataframe of the drop results
a <- as.data.frame(qtlfit$result.drop)
# Check significance
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Check significance
a <- data.frame(name=rownames(a[a$sig==0,]))
# Separate the column name into sections
a <- tidyr::separate(data = a,
col = "name",
into = c("chr", "trash", "pos"),
sep = "-")
# Make sure position is a number
a$pos <- as.numeric(a$pos)
# Make the chromosome object from the trait
a$chr <- gsub(paste("MQM", j, "_", sep = ""),"",a$chr)
# Drop the insignificant QTL from the object
madeqtl <- dropfromqtl(madeqtl,
chr = a$chr,
pos = a$pos)
# Add the significant QTL to the object
madeqtl <- addtoqtl(cross_file,
qtl = madeqtl,
chr = qtl_check$chr,
pos = qtl_check$pos,
qtl.name = qtl_check$name)
# Add this QTL object to the list of list
results[[paste("MQM", j, sep="")]]$qtl[[i]] <- madeqtl
# Remove temporary dataframes
remove(a, c, p)
}else{
# Announce
print(paste("There was only one new QTL identified for ",
i,
" in ",
paste("MQM", j, sep = ""),
"... Checking significance!",
sep = ""))
# If this QTL is significant
if(qtlfit$result.full[1,6]<0.05){
# Announce
print("New QTL is significant, placing in total model!")
# Add that significant peak to the QTL object
madeqtl <- addtoqtl(cross_file,
qtl = madeqtl,
chr = qtl_check$chr,
pos = qtl_check$pos,
qtl.name = qtl_check$name)
# Add the object to the list of list
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- madeqtl
}else{
# Make the multiple QTL model NULL
results[[paste("MQM", j, sep = "")]]$qtl[[i]] <- NULL
# Announce
print(paste("There were no new QTL identified for", i, "concluding loop."))
# Break loop
break
}
}
}
# Add result to object to check
qtl_check <- results[[paste("MQM", j, sep="")]]$qtl[[i]]
# Add 1 to the j vector to indicate what round we are on
j=j+1
}
# Check
if(j-1==0){
# Pull and make final qtl object
finalqtl <- results$IM$qtl[[i]]
}else{
# Pull and make final qtl object
finalqtl <- results[[paste("MQM", j-1, sep="")]]$qtl[[i]]
}
# Check for significance
qtlfit <- fitqtl(cross_file,
qtl = finalqtl,
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Rename the QTL
a <- finalqtl
a <- data.frame(summary(a))
a <- tidyr::separate(a, col = "name", into=c("model", "trash"), sep = "_")
a$name <- paste(a$model, "_", a$chr, "-pos-", a$pos, sep = "")
finalqtl$name = a$name
remove(a)
# Announce
print("Filtering insignificant markers...")
# If there are drop one analysis results
if(!is.null(qtlfit$result.drop)){
# Pull results into a dataframe
a <- as.data.frame(qtlfit$result.drop)
# Find the significant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Select the insignificant markers
a <- data.frame(name=rownames(a[a$sig==0,]))
# Remove those QTL from the final QTL object
finalqtl <- dropfromqtl(finalqtl,
qtl.name = a$name)
# Add this to the list of list
results$final$qtl_unrefine[[i]] <- finalqtl
# Remove the object
remove(a, finalqtl)
}else{
# Put the final object in the list of list
results$final$qtl_unrefine[[i]] <- finalqtl
# Remove the object finalqtl
remove(finalqtl)
}
# Announce
print("Refining QTL position...")
# Refine the QTL with the refine
results$final$qtl_refine[[i]] <- refineqtl(cross = cross_file,
pheno.col = i,
qtl = results$final$qtl_unrefine[[i]],
verbose = FALSE,
method = "hk")
# Rename the QTL
a <- results$final$qtl_refine[[i]]
a <- data.frame(summary(a))
a <- tidyr::separate(a, col = "name", into=c("model", "trash"), sep = "_")
a$name <- paste(a$model, "_", a$chr, "-pos-", a$pos, sep = "")
results$final$qtl_refine[[i]]$name <- a$name
remove(a)
# Final significance check
qtlfit <- fitqtl(cross_file,
qtl = results$final$qtl_refine[[i]],
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Put the final QTL object into finalqtl
finalqtl <- results$final$qtl_refine[[i]]
# If there are drop one analysis results
if(!is.null(qtlfit$result.drop)){
# Pull results into a dataframe
a <- as.data.frame(qtlfit$result.drop)
# Find the significant QTL
a$sig <- ifelse(a$`Pvalue(F)`<0.05, 1, 0)
# Select the insignificant markers
a <- data.frame(name=rownames(a[a$sig==0,]))
# Remove those QTL from the final QTL object
finalqtl <- dropfromqtl(finalqtl,
qtl.name = a$name)
# Add this to the list of list
results$final$qtl_refine_filt[[i]] <- finalqtl
# Remove the object
remove(a, finalqtl)
}else{
# Put the final object in the list of list
results$final$qtl_refine_filt[[i]] <- finalqtl
# Remove the object finalqtl
remove(finalqtl)
}
# Create an object for support LOD internvals
droplodint<-c()
#create QTL 1.5 drop LOD intervals
for(z in results$final$qtl_refine_filt[[i]]$name){
# Pull data for specific QTL
a <- data.frame(name = z)
# Pull the scan name
a <- tidyr::separate(data = a,
col = "name",
into = c("scan", "other"),
sep = "_",
remove = FALSE)
# Pull the position name
a <- tidyr::separate(data = a,
col = "other",
into = c("chr", "trash", "pos"),
sep = "-",
remove = TRUE)
# Pull the relevant scan
b <- results[[a$scan]]$scan[[i]]
# Find the nearest sudo marker to the LOD peak
c <- find.pseudomarker(cross = cross_file,
chr = a$chr,
pos = as.numeric(a$pos))
# Pull the confidence interval for 1.5 LOD drop
d <- lodint(results = b,
chr = a$chr,
qtl.index = c,
drop = 1.5,
lodcolumn = 1,
expandtomarkers = TRUE)
# Create a dataframe with that data
d <- data.frame(name = z,
trait = i,
chr = unique(d$chr),
start_marker = rownames(d)[1],
start = d[1,2],
mid_marker = rownames(d)[2],
mid_pos = d[2,2],
stop_marker = rownames(d)[3],
stop = d[3,2])
# Bind into lodint object
droplodint <- rbind(droplodint, d)
# Remove temporary data
remove(a,b,c,d)
}
# Place in results
results$final$qtl_support_intervals[[i]] <- droplodint
# Remove
remove(droplodint)
}
## [1] "------------ Interval Mapping of VR_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait VR_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 10 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for VR_KIN20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for VR_KIN20 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait VR_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for VR_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait VR_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of VR_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait VR_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for VR_WAR20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait VR_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for VR_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_ME in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_ME in MQM3... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 10 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_KIN19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 10 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_KIN19 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 10 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_RAL19 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_WAR19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for FDK_WAR19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_WAR19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of FDK_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait FDK_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for FDK_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_ME ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_ME in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_ME --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 8 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_ME aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_KIN19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 10 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_KIN19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_KIN20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 3 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Checking drop-one analysis for significance"
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_KIN20 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_KIN20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_KIN20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_RAL19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_RAL19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_RAL19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_RAL20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 9 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 6 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 15 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_RAL20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 15 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_RAL20 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_RAL20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 15 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_RAL20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_WAR19 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_WAR19 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_WAR19 in MQM2... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR19 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_WAR19 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "------------ Interval Mapping of DON_WAR20 ------------"
## [1] "Interval mapping..."
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 2 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing covariates.
## [1] "Done"
## [1] "Permutational interval mapping..."
## -Running permutations via a cluster of 11 nodes.
## [1] "Done"
## [1] "Plotting..."
## [1] "Done"
## [1] "Defining QTL..."
## [1] "Done"
## [1] "Drawing QTL..."
## [1] "Done"
## [1] "Running drop-one QTL analysis to check significance..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "Drawing new QTL"
## [1] "Done"
## [1] "Running drop-one QTL analysis..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "There was only one new QTL identified for DON_WAR20 in MQM1... Checking significance!"
## [1] "New QTL is significant, placing in total model!"
## [1] "-------- Performing Additional QTL Scan for Trait DON_WAR20 --------"
## Warning in addqtl(cross = cross_file, pheno.col = i, qtl = qtl_check, covar = qtl_covariates, : Dropping 9 individuals with missing phenotypes.
## [1] "Plotting..."
## [1] "Done"
## [1] "There were no new QTL identified for DON_WAR20 aborting loop"
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## [1] "Filtering insignificant markers..."
## [1] "Refining QTL position..."
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
# Rename results
results_VR_FDK_DON <- results
# Remove results
remove(results)
Visualize FHB, FDK, and DON QTL
#make object for ggplot
traits <- names(cross_file$pheno)[c(grep("VR", names(cross_file$pheno)),
grep("FDK", names(cross_file$pheno)),
grep("DON", names(cross_file$pheno)))]
# Make a color pallet
color_palette <- c(
# "HD_ME" = "#0000FF", # Blue
# "HD_RAL19" = "#2F4F4F", # DarkSlateGray
# "HD_RAL20" = "#00FFFF", # Cyan
# "HD_KIN20" = "#1E90FF", # DodgerBlue
# "HD_WAR20" = "#5F9EA0", # CadetBlue
# "PH_ME" = "#EE82EE", # Violet
# "PH_RAL20" = "#BA55D3", # MediumOrchid
# "PH_KIN20" = "#9932CC", # DarkOrchid
"VR_ME" = "#FF0000", # Red
"VR_KIN19" = "#8B0000", # DarkRed
"VR_KIN20" = "#B22280", # Firebrick
"VR_RAL19" = "#A0522D", # Brown
"VR_RAL20" = "#FF4500", # OrangeRed
"VR_WAR20" = "#FF6347", # Tomato
"FDK_ME" = "#FFFF00", # Yellow
"FDK_KIN19" = "#B8860B", # DarkGoldenrod
"FDK_KIN20" = "#FFD700", # Gold
"FDK_RAL19" = "#DAA520", # Goldenrod
"FDK_RAL20" = "#F0E68C", # Khaki
"FDK_WAR19" = "#EEE8AA", # PaleGoldenrod
"FDK_WAR20" = "#8B864E", # Khaki4
"DON_ME" = "#00FF00", # DarkGreen
"DON_KIN19" = "#66CD00", # Chartreuse3
"DON_KIN20" = "#556B2F", # DarkOliveGreen
"DON_RAL19" = "#8FBC8F", # DarkSeaGreen
"DON_RAL20" = "#6B8E23", # OliveDrab
"DON_WAR19" = "#98FB98", # PaleGreen
"DON_WAR20" = "#00FF7F" # SpringGreen1
)
# Make temp list out of results
dat_temp <- results_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_"))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j = j + 1
}
# Create final plot
a <- (finalplot$chr_1A+
finalplot$chr_2A+
finalplot$chr_2B+
finalplot$chr_2D+
finalplot$chr_3A+
finalplot$chr_3B+
finalplot$chr_4A+
finalplot$chr_4B+
finalplot$chr_5A+
finalplot$chr_5B+
finalplot$chr_7A+
finalplot$chr_7D)+
plot_layout(guides = "collect", nrow = 4)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH), Heading Date (HD), Visual Rating (VR), Fusarium Damaged Kernels (FDK), and Deoxynivalenol Content (DON)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_vr_fdk_don_with_adjustment.jpg",
width = 20,
height = 15,
dpi = 320,
units = "in")
# Remove
remove(dat_ggplot, dat_temp)
Visualize FHB, FDK, and DON QTL for ME only
# Pull traits
traits<-c("VR_ME", "FDK_ME", "DON_ME")
# Make a color pallet
color_palette <- c(
# "Heading Date" = "#0000FF", # Blue
# "Plant Height" = "#EE82EE", # Violet
"Visual Rating" = "#FF0000", # Red
"Fusarium Damaged Kernels" = "#FFFF00", # Yellow
"Deoxynivalenol Content" = "#00FF00" # Green
)
# Make temp list out of results
dat_temp <- results_VR_FDK_DON
# Make object for ggplot formatted data
dat_ggplot <- c()
# For each trait
for(i in traits){
# Pull a temporary dataframe
a <- dat_temp$final$qtl_support_intervals[[i]]
# Rbind on results
dat_ggplot <- rbind(dat_ggplot, a)
# Remove
remove(a)
}
# Get a dataframe of the markers across the genome for this image
d <- dat_temp$IM$scan$VR_ME %>%
filter(chr %in% dat_ggplot$chr)
# Mutate chromosome so that it is both chromosome and trait
dat_ggplot <- dat_ggplot %>%
mutate(chr = paste(chr, trait, sep = "_")) %>%
mutate(trait=ifelse(trait=="HD_ME", "Heading Date",
ifelse(trait=="PH_ME", "Plant Height",
ifelse(trait=="VR_ME", "Visual Rating",
ifelse(trait=="FDK_ME", "Fusarium Damaged Kernels",
ifelse(trait=="DON_ME", "Deoxynivalenol Content", "ERROR"))))))
# Create a list for images
finalplot <- list()
# Set an external vector
j = 1
# For i in unique chromosomes
for(i in unique(d$chr)){
# Pull a temporary dataframe
a <- dat_ggplot[grep(i, dat_ggplot$chr),]
# Redefine trait with color pallet levels
a$trait <- factor(a$trait, levels = names(color_palette))
# Only pull the chromosome listed in object d from above
b <- d[d$chr==i,]
# Remove all sudo-markers from b
b <- b[-grep("loc", rownames(b)),]
# Move the rownames into a column called markers
b <- rownames_to_column(b, var = "marker")
# Make an object called label that is made from the covariates on that chromsomes
Label<-colnames(covar_markers)[grep(i, colnames(covar_markers))]
# If the external variable j is equal to 1
if(j==1){
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank())+ # Get rid of the x-axis line
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment", # Name the legend
y = "cM") # Lable the y-axis as "cM"
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
# If j is not equal to 1
}else{
# Create first plot
c <- ggplot(data = b, aes(x = chr, y = pos))+ # Make plot out of object b
geom_point(size = 2)+ # Make points that represent the markers
geom_line(linewidth = 1)+ # Make a line that represents the length of the linkage group
scale_y_reverse(limits = c(250,0), breaks = seq(250, 0, by = -10))+ # Scale the y axis
scale_color_manual(values = color_palette, # Set the values of the color palette
drop = FALSE)+ # and make it so that no levels are dropped
geom_segment(data = a, # Read in data
aes(x = chr, # Make the x the "chromosome"
xend = chr, # Make the ending point that same "chromosome"
y = start, # Make the start the first position
yend = stop, # Make the stop the end positoin
color = trait), # Make the color related to the trait
linewidth = 2, # Set line width to 2
show.legend = TRUE)+ # Visualize QTL confidence intervals
theme_classic()+ # Add the classic theme
theme(axis.title.x = element_blank(), # Get rid of the x-axis title
axis.text.x = element_blank(), # Get rid of the x-axis text
axis.ticks.x = element_blank(), # Get rid of the x-axis ticks
axis.line.x = element_blank(), # Get rid of the x-axis line
axis.title.y = element_blank())+ # Get rid of the y-axis title
labs(title = i, # Make the title of the graph the name of the chromosome
color = "Trait Within Environment") # Name the legend
# If label exists
if(length(Label)>0){
# Add a green point for HD and PH covariates
c <- c+
geom_point(data = d[rownames(d) %in% Label,],
aes(x = chr, y = pos),
color = "green",
size = 4,
shape = 17)
}
}
# Add to the object finalplot and name it
finalplot[[paste("chr",i,sep="_")]] <- c
# Remove temporary dataframes
remove(a,b,c)
# Add 1 to j
j = j + 1
}
# Create final plot
a <- (finalplot$chr_1A+
finalplot$chr_2A+
finalplot$chr_2B+
finalplot$chr_2D+
finalplot$chr_3A+
finalplot$chr_3B+
finalplot$chr_4A+
finalplot$chr_4B+
finalplot$chr_5A+
finalplot$chr_5B+
finalplot$chr_7A+
finalplot$chr_7D)+
plot_layout(guides = "collect", nrow = 4)+
plot_annotation(title = "1.5-LOD Support Intervals",
subtitle = "Plant Height (PH), Heading Date (HD), Visual Rating (VR), Fusarium Damaged Kernels (FDK), and Deoxynivalenol Content (DON)")
# Visualize final plot
a
# Save output
ggsave(plot = a,
filename = "support_intervals_vr_fdk_don_with_adjustment_ME_only.jpg",
width = 20,
height = 15,
dpi = 320,
units = "in")
# Remove
remove(dat_ggplot, dat_temp)
Percent variance calculations - VR, FDK, and DON with
adjustment
# Pull all traits
traits <- names(cross_file$pheno)[c(grep("VR", names(cross_file$pheno)),
grep("FDK", names(cross_file$pheno)),
grep("DON", names(cross_file$pheno)))]
# Make an object for support interval
final_qtls <- c()
# For i in trait names
for(i in traits){
# Pull temporary dataframe
a <- results_VR_FDK_DON$final$qtl_support_intervals[[i]]
# Format final QTL object
final_qtls <- rbind(final_qtls, a)
# Remove final object
remove(a)
}
# Make an object for information
qtl_info_drop_one_adj <- c()
qtl_info_full_adj <- c()
# For i in final_qtls object
for(i in traits){
# Pull data
a <- results_VR_FDK_DON$final$qtl_refine_filt[[i]]
# If a is NULL
if(is.null(a)){
# Move to next trait
next
}
# Fit QTL
b <- fitqtl(cross_file,
qtl =results_VR_FDK_DON$final$qtl_refine_filt[[i]],
pheno.col = i,
get.ests = T,
model = "normal",
method = "hk")
# Get full model
c <- as.data.frame(b$result.full)
# Move effect into column name
c$effect <- rownames(c)
rownames(c) <- NULL
# Get drop one
d <- as.data.frame(b$result.drop)
# Move QTL name into column
d$name <- rownames(d)
rownames(d) <- NULL
# Bind in data
e <- results_VR_FDK_DON$final$qtl_support_intervals[[i]]
# Left join
d <- left_join(d, e, by = "name")
# Add trait names to both full model
c$trait <- i
# Bind in data
qtl_info_drop_one_adj <- rbind(qtl_info_drop_one_adj, d)
qtl_info_full_adj <- rbind(qtl_info_full_adj, c)
# Remove
remove(a,b,c,d,e)
}
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 1 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 3 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 9 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 2 individuals with missing phenotypes.
# Write file
write.csv(qtl_info_drop_one_adj,
"vr_fdk_don_drop_one_qtl_info_with_adjustment.csv",
row.names = FALSE)
# Write file
write.csv(qtl_info_full_adj,
"vr_fdk_don_full_model_qtl_info_with_adjustment.csv",
row.names = FALSE)
Save analysis image
# Save image
save.image("Final_Analysis.RData")
Create marker table
# First report
GAWN_2015 <- readxl::read_excel("2015 5ST MDXN GAWN SUN Marker Report.xlsx",
sheet = "Table 1.4 GAWN Summary",
skip = 4,
.name_repair = 'minimal')
GAWN_2015 <- GAWN_2015[, colSums(is.na(GAWN_2015)) != nrow(GAWN_2015)]
GAWN_2015 <- GAWN_2015[,-c(1:4)]
GAWN_2015 <- as.data.frame(GAWN_2015[GAWN_2015$`Cultivar/Designation`=="GA06493-13LE6",])
# Second report
SUWWSN_2016 <- readxl::read_excel("2016 NUWWSN PNUWWSN SUWWSN Marker Report.xlsx",
sheet = "Table 1.3 SUWWSN summary",
skip = 4,
.name_repair = 'minimal')
SUWWSN_2016 <- SUWWSN_2016[, colSums(is.na(SUWWSN_2016)) != nrow(SUWWSN_2016)]
SUWWSN_2016 <- SUWWSN_2016[,-c(1:4)]
SUWWSN_2016 <- as.data.frame(SUWWSN_2016[SUWWSN_2016$`Cultivar/Designation`=="NC13-20076",])
# Select calls
GAWN_2015 <- GAWN_2015[,c(1,
grep("RHT", colnames(GAWN_2015), ignore.case = TRUE),
grep("PPD", colnames(GAWN_2015), ignore.case = TRUE),
grep("VRN", colnames(GAWN_2015), ignore.case = TRUE))]
SUWWSN_2016 <- SUWWSN_2016[,c(1,
grep("RHT", colnames(SUWWSN_2016), ignore.case = TRUE),
grep("PPD", colnames(SUWWSN_2016), ignore.case = TRUE),
grep("VRN", colnames(SUWWSN_2016), ignore.case = TRUE))]
# Format
GAWN_2015 <- GAWN_2015[1,c(1:6, 9:ncol(GAWN_2015))]
GAWN_2015[1,1:ncol(GAWN_2015)] <- c("GA06493-13LE6",
"Tall",
"Dwarf",
"Photoperiod Sensitive",
"Photoperiod Sensitive",
"Photoperiod Insensitive",
"Long Vernalization",
"Long Vernalization",
"Long Vernalization")
SUWWSN_2016 <- SUWWSN_2016[1,c(1:6, 10:ncol(SUWWSN_2016))]
SUWWSN_2016[1,] <- c("NC13-20076",
"Tall",
"Dwarf",
"Photoperiod Sensitive",
"Photoperiod Sensitive",
"Photoperiod Insensitive",
"Short Vernalization (2 Copies)",
"Long Vernalization",
"Long Vernalization")
# Marker Report
marker_report <- rbind(SUWWSN_2016, GAWN_2015)
marker_report <- as.data.frame(t(marker_report))
colnames(marker_report) <- marker_report[1,]
marker_report <- marker_report[-1,]
marker_report$Gene <- rownames(marker_report)
marker_report <- marker_report[, c("Gene", "NC13-20076", "GA06493-13LE6")]
rownames(marker_report) <- NULL
write.csv(marker_report,
"parental_marker_report.csv",
row.names = FALSE)
Make markdowns of supplemental material
Supplemental Information 1
# Create the Markdown content
markdown_content <- c("
---
title: \"Supplemental Information 1\"
output: pdf_document
---
## Description
Interval mapping (IM) and multiple-quantitative-trait-locus mapping (MQM) scans for heading date (HD) and plant height (PH), Fusarium head blight (FHB) visual ratings (VR), Fusarium damaged kernels (FDK), and deoxynivalenol (DON) content quantitative trait loci (QTL). Presented are the results of scans performed without heading date and plant height marker covariates. In the title of each graph is displayed which type of scan (IM vs MQM) and the trait which the scan belongs to. The number following MQM titles indicates which round of MQM the scan belongs to (e.g., MQM 2 is the second round of multiple QTL mapping). The y-axis displays the likelihood of odds (LOD) score of every position across the genome. The dotted line denotes the 1000 permutation significance threshold at alpha equals 0.05. If the significance threshold is not displayed within the graph, this indicates that all peaks detected in the QTL scan were below the significance threshold. This is usually apparent in the last MQM scan performed. The x-axis displays each linkage group, designated by their corresponding chromosome names (e.g., 1A, 1B, 1D, etc.). The rug of hash marks denotes the cM position of each marker in the recombination map.
")
for(i in traits){
# Pull files
temp1 <- list.files()
# Grep files out
temp1 <- temp1[grep(i, temp1)]
temp1 <- temp1[grep(".jpg", temp1)]
temp1 <- temp1[-grep("adjustment", temp1, ignore.case = TRUE)]
# Make new line
temp2 <- unlist(str_split(i, "_"))
temp2[temp2=="HD"] <- "Heading Date"
temp2[temp2=="PH"] <- "Plant Height"
temp2[temp2=="VR"] <- "Visual Ratings"
temp2[temp2=="FDK"] <- "Fusarium Damaged Kernels"
temp2[temp2=="DON"] <- "Deoxynivalenol Content"
temp2[temp2=="KIN19"] <- "Kinston, NC - 2019"
temp2[temp2=="KIN20"] <- "Kinston, NC - 2020"
temp2[temp2=="RAL19"] <- "Raleigh, NC - 2019"
temp2[temp2=="RAL20"] <- "Raleigh, NC - 2020"
temp2[temp2=="WAR19"] <- "Warsaw, VA - 2019"
temp2[temp2=="WAR20"] <- "Warsaw, VA - 2020"
temp2[temp2=="ME"] <- "All Environments"
# Create new line for markdown
temp3 <- c("\n",
paste("##", temp2[1], ifelse(temp2[2]=="All Environments", "Across" ,"in"), temp2[2]))
# Add to that line
for(j in temp1){
# Add that line
temp4 <- c(paste("![j](",j,")", sep = ""))
# Bind in
temp3 <- c(temp3, temp4)
# Remove
remove(temp4)
}
# Bind in
markdown_content <- c(markdown_content, temp3)
# Bind in new page break
markdown_content <- c(markdown_content, "\\pagebreak")
# Remove
remove(temp1, temp2, temp3)
}
# Write the Markdown content to a file
writeLines(markdown_content, "supplemental_information_1.Rmd")
# Render the Markdown file to PDF
rmarkdown::render("supplemental_information_1.Rmd", output_format = "pdf_document")
##
##
## processing file: supplemental_information_1.Rmd
## | | | 0% | |......................................................................| 100%
## output file: supplemental_information_1.knit.md
## "C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS supplemental_information_1.knit.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output supplemental_information_1.tex --lua-filter "C:\Users\zwinn\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\zwinn\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --highlight-style tango --pdf-engine pdflatex --variable graphics --variable "geometry:margin=1in"
##
## Output created: supplemental_information_1.pdf
Supplemental Information 3
# Create the Markdown content
markdown_content <- c("
---
title: \"Supplemental Information 3\"
output: pdf_document
---
## Description
Interval mapping (IM) and multiple-quantitative-trait-locus mapping (MQM) scans for Fusarium head blight (FHB) disease resistance traits visual rating (VR), Fusarium damaged kernels (FDK), and deoxynivalenol (DON) content quantitative trait loci (QTL). In the title of each graph is displayed which type of scan (IM vs MQM) and the trait which the scan belongs to. The number following MQM titles indicates which round of MQM the scan belongs to (e.g., MQM 2 is the second round of multiple QTL mapping). The y-axis displays the likelihood of odds (LOD) score of every position across the genome. The dotted line denotes the 1000 permutation at alpha equals 0.05 significance threshold. If the significance threshold is not displayed within the graph, this indicates that all peaks detected in the QTL scan were below the significance threshold. This is usually apparent in the last MQM scan performed. The x-axis displays each linkage group, designated by their corresponding chromosome names (e.g., 1A, 1B, 1D, etc.). The rug of hash marks denotes the cM position of each marker in the recombination map.
")
for(i in traits[c(grep("VR", traits),
grep("FDK", traits),
grep("DON", traits))]){
# Pull files
temp1 <- list.files()
# Grep files out
temp1 <- temp1[grep(i, temp1)]
temp1 <- temp1[grep(".jpg", temp1)]
temp1 <- temp1[grep("adjustment", temp1, ignore.case = TRUE)]
# Make new line
temp2 <- unlist(str_split(i, "_"))
temp2[temp2=="VR"] <- "Visual Ratings"
temp2[temp2=="FDK"] <- "Fusarium Damaged Kernels"
temp2[temp2=="DON"] <- "Deoxynivalenol Content"
temp2[temp2=="KIN19"] <- "Kinston, NC - 2019"
temp2[temp2=="KIN20"] <- "Kinston, NC - 2020"
temp2[temp2=="RAL19"] <- "Raleigh, NC - 2019"
temp2[temp2=="RAL20"] <- "Raleigh, NC - 2020"
temp2[temp2=="WAR19"] <- "Warsaw, VA - 2019"
temp2[temp2=="WAR20"] <- "Warsaw, VA - 2020"
temp2[temp2=="ME"] <- "All Environments"
# Create new line for markdown
temp3 <- c("\n",
paste("##", temp2[1], ifelse(temp2[2]=="All Environments", "Across" ,"in"), temp2[2]))
# Add to that line
for(j in temp1){
# Add that line
temp4 <- c(paste("![j](",j,")", sep = ""))
# Bind in
temp3 <- c(temp3, temp4)
# Remove
remove(temp4)
}
# Bind in
markdown_content <- c(markdown_content, temp3)
# Bind in new page break
markdown_content <- c(markdown_content, "\\pagebreak")
# Remove
remove(temp1, temp2, temp3)
}
# Write the Markdown content to a file
writeLines(markdown_content, "supplemental_information_3.Rmd")
# Render the Markdown file to PDF
rmarkdown::render("supplemental_information_3.Rmd", output_format = "pdf_document")
##
##
## processing file: supplemental_information_3.Rmd
## | | | 0% | |......................................................................| 100%
## output file: supplemental_information_3.knit.md
## "C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS supplemental_information_3.knit.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output supplemental_information_3.tex --lua-filter "C:\Users\zwinn\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\zwinn\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --highlight-style tango --pdf-engine pdflatex --variable graphics --variable "geometry:margin=1in"
##
## Output created: supplemental_information_3.pdf